MID - Measurement Model Code
by Michael Demidenko
October 2022
1 Simulating data
This section fits a population model of the theoretical phenomenon
that are involved in the reward processing construct. The idea is that
there are approach and avoidance (measured phenomena) characteristics
that are associated with stimuli within/across tasks. There is no
ground truth of these processes at the individual.
Nevertheless, the phenomena are important to the broad construct of
reward processing, which within the MID task are hypothesized to reflect
the the multidimensional affective circumplex model. As formalized here,
in the MID task the contrasts reflect the phenomenon (i.e., reflective
model) as opposed of the other way, whereby the items form the
phenomenon (i.e., formativefmodel).
1.1 Specify Population Model
Start off by specifying the population model. In
this scenario, the individual runs load onto the specific Contrast and
ROI combinations. Then, the ROIs are loaded onto the factors
approach and avoidance. The approach and
avoidance are specified as negatively correlated and the factor
variances are fixed to 1.
population_model<-'
# By run loadings for bilateral regions
AWin_v_Neut_L_NAcc =~ .7*AWin_v_Neut_L_NAcc_run1 + .7*AWin_v_Neut_L_NAcc_run2
AWin_v_Neut_L_Insula =~ .7*AWin_v_Neut_L_Insula_run1 + .7*AWin_v_Neut_L_Insula_run2
BWin_v_Neut_L_NAcc =~ .7*BWin_v_Neut_L_NAcc_run1 + .7*BWin_v_Neut_L_NAcc_run2
BWin_v_Neut_L_Insula =~ .7*BWin_v_Neut_L_Insula_run1 + .7*BWin_v_Neut_L_Insula_run2
BWin_v_BLose_L_NAcc =~ .7*BWin_v_BLose_L_NAcc_run1 + .7*BWin_v_BLose_L_NAcc_run2
BWin_v_BLose_L_Insula =~ .7*BWin_v_BLose_L_Insula_run1 + .7*BWin_v_BLose_L_Insula_run2
ALose_v_Neut_L_NAcc =~ .7*ALose_v_Neut_L_NAcc_run1 + .7*ALose_v_Neut_L_NAcc_run2
ALose_v_Neut_L_Insula =~ .7*ALose_v_Neut_L_Insula_run1 + .7*ALose_v_Neut_L_Insula_run2
BLose_v_Neut_L_NAcc =~ .7*BLose_v_Neut_L_NAcc_run1 + .7*BLose_v_Neut_L_NAcc_run2
BLose_v_Neut_L_Insula =~ .7*BLose_v_Neut_L_Insula_run1 + .7*BLose_v_Neut_L_Insula_run2
BLose_v_BWin_L_NAcc =~ .7*BLose_v_BWin_L_NAcc_run1 + .7*BLose_v_BWin_L_NAcc_run2
BLose_v_BWin_L_Insula =~ .7*BLose_v_BWin_L_Insula_run1 + .7*BLose_v_BWin_L_Insula_run2
AWin_v_Neut_R_NAcc =~ .7*AWin_v_Neut_R_NAcc_run1 + .7*AWin_v_Neut_R_NAcc_run2
AWin_v_Neut_R_Insula =~ .7*AWin_v_Neut_R_Insula_run1 + .7*AWin_v_Neut_R_Insula_run2
BWin_v_Neut_R_NAcc =~ .7*BWin_v_Neut_R_NAcc_run1 + .7*BWin_v_Neut_R_NAcc_run2
BWin_v_Neut_R_Insula =~ .7*BWin_v_Neut_R_Insula_run1 + .7*BWin_v_Neut_R_Insula_run2
BWin_v_BLose_R_NAcc =~ .7*BWin_v_BLose_R_NAcc_run1 + .7*BWin_v_BLose_R_NAcc_run2
BWin_v_BLose_R_Insula =~ .7*BWin_v_BLose_R_Insula_run1 + .7*BWin_v_BLose_R_Insula_run2
ALose_v_Neut_R_NAcc =~ .7*ALose_v_Neut_R_NAcc_run1 + .7*ALose_v_Neut_R_NAcc_run2
ALose_v_Neut_R_Insula =~ .7*ALose_v_Neut_R_Insula_run1 + .7*ALose_v_Neut_R_Insula_run2
BLose_v_Neut_R_NAcc =~ .7*BLose_v_Neut_R_NAcc_run1 + .7*BLose_v_Neut_R_NAcc_run2
BLose_v_Neut_R_Insula =~ .7*BLose_v_Neut_R_Insula_run1 + .7*BLose_v_Neut_R_Insula_run2
BLose_v_BWin_R_NAcc =~ .7*BLose_v_BWin_R_NAcc_run1 + .7*BLose_v_BWin_R_NAcc_run2
BLose_v_BWin_R_Insula =~ .7*BLose_v_BWin_R_Insula_run1 + .7*BLose_v_BWin_R_Insula_run2
#Factor item loadings
Approach =~ .8*AWin_v_Neut_L_NAcc + .8*AWin_v_Neut_R_NAcc + .45*AWin_v_Neut_R_Insula +
.7*BWin_v_Neut_L_NAcc + .7*BWin_v_Neut_R_NAcc + .4*BWin_v_Neut_R_Insula +
.8*BWin_v_BLose_L_NAcc + .8*BWin_v_BLose_R_NAcc
Avoid =~ .8*ALose_v_Neut_L_Insula + .8*ALose_v_Neut_R_Insula +
.75*BLose_v_Neut_L_Insula + .75*BLose_v_Neut_R_Insula +
.8*BLose_v_BWin_L_Insula + .45*BLose_v_BWin_R_Insula
# Factor Covariances
Approach ~~ -.6*Avoid
# Fixing factor variances
Approach ~~ 1*Approach
Avoid ~~ 1*Avoid
# Factor means/intercepts
#Approach ~ 1
#Avoid ~ 1
'1.2 General samples
Using the population model, simsem
is used to create simulated data based on the population model. This
generates a fake dataset that is used to pilot the planned
CFA, ESEM, EFA and Local SEM models
In this case, 50 repetitions are simulated per model for an
approximate N sample for each study. Even though the factor
variances are specified in the population model as ‘1’, this model
fixeds all latent variables using std.lv = TRUE.
- AHRB N = 104
- MLS N = 120
- ABCD N = 1000
# Using simsem to fit population model by creating a simulated set of 50 population sets. Do this for a dummy AHRB, MLS and ABCD sample
# the samples size is mean to be comparable to what I'd estimate I'd have access to in the real data.
set.seed(25151215)
sim_AHRB <- simsem::sim(nRep = 50, model = "lavaan", n = 104,
generate = population_model, std.lv = TRUE, lavaanfun = "sem",
# std.lv ~ ix the variances of all the latent variables
dataOnly=T, meanstructure = FALSE, seed=123)
sim_MLS <- simsem::sim(nRep = 50, model = "lavaan", n = 120,
generate = population_model, std.lv = TRUE, lavaanfun = "sem",
dataOnly=T, meanstructure = FALSE, seed=123)
sim_ABCD <- simsem::sim(nRep = 50, model = "lavaan", n = 1000,
generate = population_model, std.lv = TRUE, lavaanfun = "sem",
dataOnly=T, meanstructure = FALSE, seed=123)Average each repeptition for sample simulated. For example, after 50
repitions of 1000 participants for the population model of ABCD sample,
an average estimate is derived using aaply. For
each study, the set variable is created to differentiate
which sample the data is associated with (i.e., grouping variable).
# for each simulate sets (50) of data, taking the mean of sets to create final study specific datasets, AHRB (3), MLS (2), ABCD (1)
sim_AHRB_data <- data.frame(aaply(laply(sim_AHRB, as.matrix), c(2,3), mean))
sim_AHRB_data$set <-3
sim_MLS_data <- data.frame(aaply(laply(sim_MLS, as.matrix), c(2,3), mean))
sim_MLS_data$set <-2
sim_ABCD_data <- data.frame(aaply(laply(sim_ABCD, as.matrix), c(2,3), mean))
sim_ABCD_data$set <-1Next, row bind the data sets to form one complete data
#Combining the dataset to create a 2259 (combined participants) x 25 (variables)
brain_set <- rbind(sim_AHRB_data,sim_MLS_data,sim_ABCD_data)1.3 Correlation matrix of data
Here, a combination of rcorr and corrplot is used visualize the data.
# Using Hmisc to create a 24x24 matrix for a list (3) that contains: the pearson's r corr, sample size (N), and significance (p).
Brain_corr = rcorr(as.matrix(subset(brain_set,select=-c(set))), # excluding the set of data related to sample
type = "pearson")
# Using corrplot() to create heatmap of the data.
par(mfrow=c(1,1))
corrplot(Brain_corr$r, type = "upper",
order = 'hclust',
method = "color",
tl.cex = 0.5, tl.col = 'black',
cl.pos = 'r', tl.pos = 'lt', outline = TRUE,
col=colorRampPalette(c("navyblue","white","red2"))(100),# colours http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
mar = c(2,.15,.25,.15)#bottom, left, top and right,
)
2 Running [restricted] CFA + Multigroup CFA
Run the CFA multi-group analysis for the three datasets. Multi-group CFA tests the measurement invariance across defined groups to determine whether soft and strict invariance criteria are met and the degree to which the derive estimates for an item in one study can be compared to the same item in another sample. In this case, the focus is on the configural (structure) and metric invariance (loadings). In short, this model evalutes whether factor structure and loadings for the approach and avoidance model are invariant (dont significant differ) across the samples.
Code here is based on measurement invariance models from Maasen et al. 2019, Measurement invariance presentation from Kate Xu and Multi-group CFA tutorial from Hirschfeld & Brachel (2014).
The issue of multi-group is invariance what is discussed in Borsboom (2006). In short, (1) Interpretation of group differences on observed scores DEPENDS on the invariance of measurement models & (2) many make conclusions without doing a single test of measurement invariance.
2.1 Not used CFA Model
The initial idea was to fit values from each run (e.g., run 1 and run 2 for RightNACC_Contrast1) onto a single indicator (i.e., RightNAcc_Contrast1) to account for reliability across runs. However, the run data often consist of lower ICC values and so the model may have trouble converging. This method will also create a path-to-sample ratio of 1 to <=3. This is pretty low and may produce unreliable estimates in the maximum likelihood framework (even if using robust estimate) (see Kline 2015 book on Principles and Practice of Structural Equation Modeling)
Below is an example of a model that will not be used due to smaller N but may be in future large N samples.
MID_model_notused <-'
# Run Loadings
# To impose equality contraints across runs? Too many runs to estimates with to few data? Not using here at this time
AWin_v_Neut_R_NAcc =~ AWin_v_Neut_R_NAcc_run1 + AWin_v_Neut_R_NAcc_run2
AWin_v_Neut_R_Insula =~ AWin_v_Neut_R_Insula_run1 + AWin_v_Neut_R_Insula_run2
BWin_v_Neut_R_NAcc =~ BWin_v_Neut_R_NAcc_run1 + BWin_v_Neut_R_NAcc_run2
BWin_v_Neut_R_Insula =~ BWin_v_Neut_R_Insula_run1 + BWin_v_Neut_R_Insula_run2
BWin_v_BLose_R_NAcc =~ BWin_v_BLose_R_NAcc_run1 + BWin_v_BLose_R_NAcc_run2
BWin_v_BLose_R_Insula =~ BWin_v_BLose_R_Insula_run1+ BWin_v_BLose_R_Insula_run2
ALose_v_Neut_R_NAcc =~ ALose_v_Neut_R_NAcc_run1 + ALose_v_Neut_R_NAcc_run2
ALose_v_Neut_R_Insula =~ ALose_v_Neut_R_Insula_run1+ ALose_v_Neut_R_Insula_run2
BLose_v_Neut_R_NAcc =~ BLose_v_Neut_R_NAcc_run1 + BLose_v_Neut_R_NAcc_run2
BLose_v_Neut_R_Insula =~ BLose_v_Neut_R_Insula_run1+ BLose_v_Neut_R_Insula_run2
BLose_v_BWin_R_NAcc =~ BLose_v_BWin_R_NAcc_run1 + BLose_v_BWin_R_NAcc_run2
BLose_v_BWin_R_Insula =~ BLose_v_BWin_R_Insula_run1+ BLose_v_BWin_R_Insula_run2
# Using [m-x] to impose simple equality constraints on individual runs loading onto avg run values
AWin_v_Neut_L_NAcc =~ AWin_v_Neut_L_NAcc_run1 + AWin_v_Neut_L_NAcc_run2
AWin_v_Neut_L_Insula =~ AWin_v_Neut_L_Insula_run1 + AWin_v_Neut_L_Insula_run2
BWin_v_Neut_L_NAcc =~ BWin_v_Neut_L_NAcc_run1 + BWin_v_Neut_L_NAcc_run2
BWin_v_Neut_L_Insula =~ BWin_v_Neut_L_Insula_run1 + BWin_v_Neut_L_Insula_run2
BWin_v_BLose_L_NAcc =~ BWin_v_BLose_L_NAcc_run1 + BWin_v_BLose_L_NAcc_run2
BWin_v_BLose_L_Insula =~ BWin_v_BLose_L_Insula_run1+ BWin_v_BLose_L_Insula_run2
ALose_v_Neut_L_NAcc =~ ALose_v_Neut_L_NAcc_run1 + ALose_v_Neut_L_NAcc_run2
ALose_v_Neut_L_Insula =~ ALose_v_Neut_L_Insula_run1+ ALose_v_Neut_L_Insula_run2
BLose_v_Neut_L_NAcc =~ BLose_v_Neut_L_NAcc_run1 + BLose_v_Neut_L_NAcc_run2
BLose_v_Neut_L_Insula =~ BLose_v_Neut_L_Insula_run1+ BLose_v_Neut_L_Insula_run2
BLose_v_BWin_L_NAcc =~ BLose_v_BWin_L_NAcc_run1 + BLose_v_BWin_L_NAcc_run2
BLose_v_BWin_L_Insula =~ BLose_v_BWin_L_Insula_run1+ BLose_v_BWin_L_Insula_run2
Approach =~ AWin_v_Neut_L_NAcc + AWin_v_Neut_R_NAcc + AWin_v_Neut_R_Insula +
BWin_v_Neut_L_NAcc + BWin_v_Neut_R_NAcc + BWin_v_Neut_R_Insula +
BWin_v_BLose_L_NAcc + BWin_v_BLose_R_NAcc
Avoid =~ ALose_v_Neut_L_Insula + ALose_v_Neut_L_Insula +
BLose_v_Neut_L_Insula + BLose_v_Neut_R_Insula +
BLose_v_BWin_L_Insula + BLose_v_BWin_R_Insula
'2.2 Used CFA model
The below specified model will be used. The number of estimate parameters are fewer and may be more appropriate for the theoretical model. This model may result in few convergence issues if the number of participants ends up to be few and the coefficients/estimates are lower.
MID_model <-'
# Factor loadings
Approach =~ AWin_v_Neut_L_NAcc_run1 + AWin_v_Neut_R_NAcc_run1 + AWin_v_Neut_R_Insula_run1 +
BWin_v_Neut_L_NAcc_run1 + BWin_v_Neut_R_NAcc_run1 + BWin_v_Neut_R_Insula_run1 +
BWin_v_BLose_L_NAcc_run1 + BWin_v_BLose_R_NAcc_run1 +
AWin_v_Neut_L_NAcc_run2 + AWin_v_Neut_R_NAcc_run2 + AWin_v_Neut_R_Insula_run2 +
BWin_v_Neut_L_NAcc_run2 + BWin_v_Neut_R_NAcc_run2 + BWin_v_Neut_R_Insula_run2 +
BWin_v_BLose_L_NAcc_run2 + BWin_v_BLose_R_NAcc_run2
Avoid =~ ALose_v_Neut_L_Insula_run1 + ALose_v_Neut_L_Insula_run1 +
BLose_v_Neut_L_Insula_run1 + BLose_v_Neut_R_Insula_run1 +
BLose_v_BWin_L_Insula_run1 + BLose_v_BWin_R_Insula_run1 +
ALose_v_Neut_L_Insula_run2 + ALose_v_Neut_R_Insula_run2 +
BLose_v_Neut_L_Insula_run2 + BLose_v_Neut_R_Insula_run2 +
BLose_v_BWin_L_Insula_run2 + BLose_v_BWin_R_Insula_run2
'2.3 Running CFA: Three Samples
Below is the CFA model that is used to test the proposed restricted
model (see Figure 1 in the manuscript). The CFA fitting procedure is
consistent with the description here. For each CFA
model, the full sample is filtered for each type sample, e.g. AHRB, MLS,
ABCD. The std.lv= = TRUE constrain the latent factor
variances to 1. The estimator being used is MLR, a
maximum likelihood robust estimator. In addition to a model for each
sample, a CFA model is estimated for the complete data (i.e., all three
datasets).
# For starters, the CFA is estimated for each sample that is simulated (i.e., AHRB [1], MLS [2], ABCD [3])
AHRB_cfa <- cfa(model = MID_model, data = subset(brain_set %>% filter(set==3)),
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE) # fixing latent variances to 1
MLS_cfa <- cfa(model = MID_model, data = subset(brain_set %>% filter(set==2)),
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE)
ABCD_cfa <- cfa(model = MID_model, data = subset(brain_set %>% filter(set==1)),
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE)
all_cfa <- cfa(model = MID_model, data = brain_set,
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE)2.4 Fitting Configural CFA
Here, the configular multigroup model is fit. As
described in D’Urso et
al. (2022) measurement invariance pre-print, the configural model
tests:
is the structure of the factors is invariannt across the samples (‘set’). In other words, if we a priori propose a two-factor structure (FA 1 = approch and FA 2 = Avoidance), does this two factor structure represent the between-person variability in the items that reflect the factors across each sample?
If the variability in one sample suggests a one, three, or four factor structure, this will be degrade the fit statistics.
A pre-specified CFA model is used to evaluate whether the
measures/items that reflect the factor are the same across groups.
group= 'set' is used to define the grouping variable. All
loadings and intercepts are free to vary across groups, and the factor
variance is set to ‘1’ via std.lv = TRUE
configural_cfa <- cfa(model = MID_model, data = brain_set, group = 'set',
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE)2.5 Fitting Metric CFA
After fitting the CFA configurial (factor structure) invariance, if the model fit is not poor, then the next step is to test the metric invariance. Metric invariance tests:
are the loadings are consistent across the groups. In other words,are the phenomena (i.e., approach and avoidance) reflected by the same pattern across the measures/items?
One cause for concern may be that the phenomenon are not invariant across age groups, in that the items/measures (ROIs for a given contrast) do not load in the same manner onto each factor. This ‘soft’ measure of invariance can determine whether the items functions differ across the items and so cannot be easily compared.
The model is fit using the same procedure as for configurial
invariance with one exception: In metric invariance the loadings group
equality constraint is added to the model via
group.equal=c("loadings"). The model fit statistics are
used to evaluate whether the fit is poor.
metric_cfa <-cfa(model = MID_model, data = brain_set,
group = 'set', group.equal=c("loadings"),
estimator = "MLR", std.lv = TRUE, meanstructure = TRUE)2.6 Extracting Fit Statistics
Once the above models are fit, the following information is pulled
out and saved into a out data frame:
- Model name
- Chi-square statistics
- Model Degrees of Freedom (df)
- Model p-value
- RMSEA
- CFI
- SRMR
- AIC
- BIC
# Below selects specific fit data as described in Maassen et al. 2019 OSF. No comparisons are made to compare models at this point.
out <- matrix(NA, ncol = 9, nrow = 7)
colnames(out) <- c("model","chisq","df","pvalue", "rmsea", "cfi", "srmr",
"AIC", "BIC")
# save fit measures from models
out[1,2:7] <- round(data.matrix(fitmeasures(AHRB_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[2,2:7] <- round(data.matrix(fitmeasures(MLS_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[3,2:7] <- round(data.matrix(fitmeasures(ABCD_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[4,2:7] <- round(data.matrix(fitmeasures(all_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[5,2:7] <- round(data.matrix(fitmeasures(configural_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[6,2:7] <- round(data.matrix(fitmeasures(metric_cfa,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
# AIC models
out[1,8] <- round(AIC(AHRB_cfa),3)
out[2,8] <- round(AIC(MLS_cfa),3)
out[3,8] <- round(AIC(ABCD_cfa),3)
out[4,8] <- round(AIC(all_cfa),3)
out[5,8] <- round(AIC(configural_cfa),3)
out[6,8] <- round(AIC(metric_cfa),3)
# BIC models
out[1,9] <- round(BIC(AHRB_cfa),3)
out[2,9] <- round(BIC(MLS_cfa),3)
out[3,9] <- round(BIC(ABCD_cfa),3)
out[4,9] <- round(BIC(all_cfa),3)
out[5,9] <- round(BIC(configural_cfa),3)
out[6,9] <- round(BIC(metric_cfa),3)
out[1:6,1] <- c("AHRB CFA","MLS CFA","ABCD CFA", "Overall CFA", "Configg MG-CFA", "Metric MG-CFA")2.7 Model Parameter Summary
Reporting standardized coefficients.
2.7.1 AHRB CFA model
##### Summarizing CFA models #####
parameters(AHRB_cfa, standardize = T)## # Loading
##
## Link | Coefficient | SE | 95% CI | z | p
## ------------------------------------------------------------------------------------------
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.48 | 0.13 | [ 0.23, 0.73] | 3.81 | < .001
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.45 | 0.10 | [ 0.26, 0.65] | 4.59 | < .001
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.33 | 0.11 | [ 0.11, 0.56] | 2.90 | 0.004
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.29 | 0.12 | [ 0.04, 0.53] | 2.31 | 0.021
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.49 | 0.14 | [ 0.21, 0.77] | 3.45 | < .001
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.26 | 0.11 | [ 0.05, 0.47] | 2.42 | 0.016
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.33 | 0.11 | [ 0.12, 0.53] | 3.10 | 0.002
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.48 | 0.15 | [ 0.19, 0.77] | 3.26 | 0.001
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.67 | 0.13 | [ 0.42, 0.93] | 5.13 | < .001
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.41 | 0.11 | [ 0.20, 0.62] | 3.78 | < .001
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.31 | 0.14 | [ 0.03, 0.58] | 2.18 | 0.029
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.19 | 0.13 | [-0.08, 0.45] | 1.39 | 0.163
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.55 | 0.13 | [ 0.31, 0.80] | 4.38 | < .001
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.12 | 0.13 | [-0.14, 0.37] | 0.91 | 0.365
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.28 | 0.10 | [ 0.09, 0.47] | 2.87 | 0.004
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.55 | 0.14 | [ 0.28, 0.82] | 4.03 | < .001
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.33 | 0.14 | [ 0.05, 0.61] | 2.30 | 0.021
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.65 | 0.09 | [ 0.47, 0.83] | 7.04 | < .001
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.43 | 0.11 | [ 0.21, 0.65] | 3.86 | < .001
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.46 | 0.12 | [ 0.23, 0.69] | 3.91 | < .001
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.28 | 0.11 | [ 0.06, 0.50] | 2.49 | 0.013
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.37 | 0.12 | [ 0.14, 0.60] | 3.12 | 0.002
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.41 | 0.09 | [ 0.23, 0.59] | 4.41 | < .001
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.65 | 0.08 | [ 0.50, 0.81] | 8.38 | < .001
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.40 | 0.12 | [ 0.15, 0.64] | 3.21 | 0.001
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.38 | 0.11 | [ 0.15, 0.60] | 3.28 | 0.001
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.27 | 0.10 | [ 0.08, 0.46] | 2.81 | 0.005
##
## # Correlation
##
## Link | Coefficient | SE | 95% CI | z | p
## ----------------------------------------------------------------------
## Approach ~~ Avoid | -0.29 | 0.16 | [-0.60, 0.02] | -1.81 | 0.0702.7.2 MLS CFA model
##### Summarizing CFA models #####
parameters(MLS_cfa, standardize = T)## # Loading
##
## Link | Coefficient | SE | 95% CI | z | p
## ------------------------------------------------------------------------------------------
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.46 | 0.14 | [ 0.18, 0.73] | 3.28 | 0.001
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.44 | 0.15 | [ 0.14, 0.74] | 2.88 | 0.004
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.10 | 0.13 | [-0.16, 0.35] | 0.75 | 0.455
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.24 | 0.11 | [ 0.03, 0.45] | 2.28 | 0.022
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.32 | 0.12 | [ 0.08, 0.56] | 2.65 | 0.008
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.27 | 0.11 | [ 0.05, 0.50] | 2.43 | 0.015
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.43 | 0.14 | [ 0.16, 0.70] | 3.17 | 0.002
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.45 | 0.10 | [ 0.26, 0.64] | 4.56 | < .001
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.47 | 0.13 | [ 0.21, 0.73] | 3.59 | < .001
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.57 | 0.13 | [ 0.32, 0.81] | 4.52 | < .001
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.10 | 0.13 | [-0.16, 0.36] | 0.72 | 0.472
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.25 | 0.13 | [-0.01, 0.51] | 1.87 | 0.061
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.30 | 0.12 | [ 0.07, 0.54] | 2.54 | 0.011
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.17 | 0.12 | [-0.06, 0.40] | 1.47 | 0.143
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.46 | 0.12 | [ 0.23, 0.69] | 3.90 | < .001
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.25 | 0.12 | [ 0.01, 0.49] | 2.06 | 0.039
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.65 | 0.18 | [ 0.30, 1.00] | 3.67 | < .001
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.28 | 0.10 | [ 0.09, 0.47] | 2.92 | 0.003
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.26 | 0.16 | [-0.06, 0.58] | 1.58 | 0.115
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.49 | 0.17 | [ 0.15, 0.83] | 2.80 | 0.005
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.23 | 0.10 | [ 0.04, 0.43] | 2.33 | 0.020
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.80 | 0.16 | [ 0.49, 1.11] | 5.00 | < .001
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.40 | 0.12 | [ 0.16, 0.65] | 3.22 | 0.001
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.36 | 0.11 | [ 0.15, 0.56] | 3.36 | < .001
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.42 | 0.14 | [ 0.13, 0.70] | 2.89 | 0.004
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.54 | 0.15 | [ 0.24, 0.84] | 3.56 | < .001
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.25 | 0.10 | [ 0.06, 0.44] | 2.59 | 0.010
##
## # Correlation
##
## Link | Coefficient | SE | 95% CI | z | p
## -----------------------------------------------------------------------
## Approach ~~ Avoid | -0.36 | 0.17 | [-0.70, -0.02] | -2.10 | 0.0362.7.3 ABCD CFA model
##### Summarizing CFA models #####
parameters(ABCD_cfa, standardize = T)## # Loading
##
## Link | Coefficient | SE | 95% CI | z | p
## ------------------------------------------------------------------------------------------
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.45 | 0.03 | [0.38, 0.52] | 13.10 | < .001
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.44 | 0.03 | [0.37, 0.50] | 13.45 | < .001
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.24 | 0.04 | [0.17, 0.31] | 6.68 | < .001
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.51 | 0.03 | [0.45, 0.57] | 17.07 | < .001
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.39 | 0.03 | [0.32, 0.45] | 12.08 | < .001
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.24 | 0.03 | [0.17, 0.31] | 6.91 | < .001
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.47 | 0.03 | [0.41, 0.53] | 14.83 | < .001
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.46 | 0.03 | [0.40, 0.53] | 14.38 | < .001
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.48 | 0.03 | [0.42, 0.55] | 14.83 | < .001
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.50 | 0.03 | [0.44, 0.55] | 17.12 | < .001
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.27 | 0.03 | [0.20, 0.34] | 7.85 | < .001
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.42 | 0.03 | [0.36, 0.49] | 13.22 | < .001
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.42 | 0.03 | [0.36, 0.48] | 13.51 | < .001
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.27 | 0.04 | [0.20, 0.34] | 7.39 | < .001
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.42 | 0.03 | [0.35, 0.48] | 12.71 | < .001
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.48 | 0.03 | [0.42, 0.55] | 14.90 | < .001
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.46 | 0.04 | [0.39, 0.53] | 13.01 | < .001
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.46 | 0.04 | [0.39, 0.53] | 13.05 | < .001
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.52 | 0.04 | [0.45, 0.59] | 14.55 | < .001
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.44 | 0.04 | [0.37, 0.51] | 12.02 | < .001
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.24 | 0.04 | [0.17, 0.32] | 6.39 | < .001
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.42 | 0.03 | [0.35, 0.48] | 12.33 | < .001
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.39 | 0.03 | [0.33, 0.45] | 12.27 | < .001
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.43 | 0.04 | [0.36, 0.50] | 11.63 | < .001
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.49 | 0.04 | [0.42, 0.56] | 13.99 | < .001
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.42 | 0.04 | [0.35, 0.49] | 11.31 | < .001
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.23 | 0.04 | [0.16, 0.31] | 6.05 | < .001
##
## # Correlation
##
## Link | Coefficient | SE | 95% CI | z | p
## -------------------------------------------------------------------------
## Approach ~~ Avoid | -0.56 | 0.03 | [-0.63, -0.50] | -16.62 | < .0012.7.4 Configural CFA model
##### Summarizing CFA models #####
parameters(configural_cfa, standardize = T)## # Loading
##
## Link | Coefficient | SE | 95% CI | z | p | Group
## ---------------------------------------------------------------------------------------------------
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.48 | 0.13 | [ 0.23, 0.73] | 3.81 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.45 | 0.10 | [ 0.26, 0.65] | 4.59 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.33 | 0.11 | [ 0.11, 0.56] | 2.90 | 0.004 | 1.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.29 | 0.12 | [ 0.04, 0.53] | 2.31 | 0.021 | 1.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.49 | 0.14 | [ 0.21, 0.77] | 3.45 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.26 | 0.11 | [ 0.05, 0.47] | 2.42 | 0.016 | 1.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.33 | 0.11 | [ 0.12, 0.53] | 3.10 | 0.002 | 1.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.48 | 0.15 | [ 0.19, 0.77] | 3.26 | 0.001 | 1.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.67 | 0.13 | [ 0.42, 0.93] | 5.13 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.41 | 0.11 | [ 0.20, 0.62] | 3.78 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.31 | 0.14 | [ 0.03, 0.58] | 2.18 | 0.029 | 1.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.19 | 0.13 | [-0.08, 0.45] | 1.39 | 0.163 | 1.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.55 | 0.13 | [ 0.31, 0.80] | 4.38 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.12 | 0.13 | [-0.14, 0.37] | 0.91 | 0.365 | 1.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.28 | 0.10 | [ 0.09, 0.47] | 2.87 | 0.004 | 1.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.55 | 0.14 | [ 0.28, 0.82] | 4.03 | < .001 | 1.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.33 | 0.14 | [ 0.05, 0.61] | 2.30 | 0.021 | 1.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.65 | 0.09 | [ 0.47, 0.83] | 7.04 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.43 | 0.11 | [ 0.21, 0.65] | 3.86 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.46 | 0.12 | [ 0.23, 0.69] | 3.91 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.28 | 0.11 | [ 0.06, 0.50] | 2.49 | 0.013 | 1.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.37 | 0.12 | [ 0.14, 0.60] | 3.12 | 0.002 | 1.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.41 | 0.09 | [ 0.23, 0.59] | 4.41 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.65 | 0.08 | [ 0.50, 0.81] | 8.38 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.40 | 0.12 | [ 0.15, 0.64] | 3.21 | 0.001 | 1.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.38 | 0.11 | [ 0.15, 0.60] | 3.28 | 0.001 | 1.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.27 | 0.10 | [ 0.08, 0.46] | 2.81 | 0.005 | 1.00
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.46 | 0.14 | [ 0.18, 0.73] | 3.28 | 0.001 | 2.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.44 | 0.15 | [ 0.14, 0.74] | 2.88 | 0.004 | 2.00
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.10 | 0.13 | [-0.16, 0.35] | 0.75 | 0.455 | 2.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.24 | 0.11 | [ 0.03, 0.45] | 2.28 | 0.022 | 2.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.32 | 0.12 | [ 0.08, 0.56] | 2.65 | 0.008 | 2.00
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.27 | 0.11 | [ 0.05, 0.50] | 2.43 | 0.015 | 2.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.43 | 0.14 | [ 0.16, 0.70] | 3.17 | 0.002 | 2.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.45 | 0.10 | [ 0.26, 0.64] | 4.56 | < .001 | 2.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.47 | 0.13 | [ 0.21, 0.73] | 3.59 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.57 | 0.13 | [ 0.32, 0.81] | 4.52 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.10 | 0.13 | [-0.16, 0.36] | 0.72 | 0.472 | 2.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.25 | 0.13 | [-0.01, 0.51] | 1.87 | 0.061 | 2.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.30 | 0.12 | [ 0.07, 0.54] | 2.54 | 0.011 | 2.00
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.17 | 0.12 | [-0.06, 0.40] | 1.47 | 0.143 | 2.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.46 | 0.12 | [ 0.23, 0.69] | 3.90 | < .001 | 2.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.25 | 0.12 | [ 0.01, 0.49] | 2.06 | 0.039 | 2.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.65 | 0.18 | [ 0.30, 1.00] | 3.67 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.28 | 0.10 | [ 0.09, 0.47] | 2.92 | 0.003 | 2.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.26 | 0.16 | [-0.06, 0.58] | 1.58 | 0.115 | 2.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.49 | 0.17 | [ 0.15, 0.83] | 2.80 | 0.005 | 2.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.23 | 0.10 | [ 0.04, 0.43] | 2.33 | 0.020 | 2.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.80 | 0.16 | [ 0.49, 1.11] | 5.00 | < .001 | 2.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.40 | 0.12 | [ 0.16, 0.65] | 3.22 | 0.001 | 2.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.36 | 0.11 | [ 0.15, 0.56] | 3.36 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.42 | 0.14 | [ 0.13, 0.70] | 2.89 | 0.004 | 2.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.54 | 0.15 | [ 0.24, 0.84] | 3.56 | < .001 | 2.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.25 | 0.10 | [ 0.06, 0.44] | 2.59 | 0.010 | 2.00
## Approach =~ AWin_v_Neut_L_NAcc_run1 | 0.45 | 0.03 | [ 0.38, 0.52] | 13.10 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 | 0.44 | 0.03 | [ 0.37, 0.50] | 13.45 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_Insula_run1 | 0.24 | 0.04 | [ 0.17, 0.31] | 6.68 | < .001 | 3.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 | 0.51 | 0.03 | [ 0.45, 0.57] | 17.07 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 | 0.39 | 0.03 | [ 0.32, 0.45] | 12.08 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_Insula_run1 | 0.24 | 0.03 | [ 0.17, 0.31] | 6.91 | < .001 | 3.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 | 0.47 | 0.03 | [ 0.41, 0.53] | 14.83 | < .001 | 3.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 | 0.46 | 0.03 | [ 0.40, 0.53] | 14.38 | < .001 | 3.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 | 0.48 | 0.03 | [ 0.42, 0.55] | 14.83 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 | 0.50 | 0.03 | [ 0.44, 0.55] | 17.12 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_Insula_run2 | 0.27 | 0.03 | [ 0.20, 0.34] | 7.85 | < .001 | 3.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 | 0.42 | 0.03 | [ 0.36, 0.49] | 13.22 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 | 0.42 | 0.03 | [ 0.36, 0.48] | 13.51 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_Insula_run2 | 0.27 | 0.04 | [ 0.20, 0.34] | 7.39 | < .001 | 3.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 | 0.42 | 0.03 | [ 0.35, 0.48] | 12.71 | < .001 | 3.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 | 0.48 | 0.03 | [ 0.42, 0.55] | 14.90 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 | 0.46 | 0.04 | [ 0.39, 0.53] | 13.01 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 | 0.46 | 0.04 | [ 0.39, 0.53] | 13.05 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 | 0.52 | 0.04 | [ 0.45, 0.59] | 14.55 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 | 0.44 | 0.04 | [ 0.37, 0.51] | 12.02 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 | 0.24 | 0.04 | [ 0.17, 0.32] | 6.39 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 | 0.42 | 0.03 | [ 0.35, 0.48] | 12.33 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 | 0.39 | 0.03 | [ 0.33, 0.45] | 12.27 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 | 0.43 | 0.04 | [ 0.36, 0.50] | 11.63 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 | 0.49 | 0.04 | [ 0.42, 0.56] | 13.99 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 | 0.42 | 0.04 | [ 0.35, 0.49] | 11.31 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 | 0.23 | 0.04 | [ 0.16, 0.31] | 6.05 | < .001 | 3.00
##
## # Correlation
##
## Link | Coefficient | SE | 95% CI | z | p | Group
## ---------------------------------------------------------------------------------
## Approach ~~ Avoid | -0.29 | 0.16 | [-0.60, 0.02] | -1.81 | 0.070 | 1.00
## Approach ~~ Avoid | -0.36 | 0.17 | [-0.70, -0.02] | -2.10 | 0.036 | 2.00
## Approach ~~ Avoid | -0.56 | 0.03 | [-0.63, -0.50] | -16.62 | < .001 | 3.002.7.5 Metric CFA model
##### Summarizing CFA models #####
parameters(metric_cfa, standardize = T)## # Loading
##
## Link | Coefficient | SE | 95% CI | z | p | Group
## ----------------------------------------------------------------------------------------------------------
## Approach =~ AWin_v_Neut_L_NAcc_run1 (.p1.) | 0.42 | 0.05 | [0.32, 0.53] | 8.02 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 (.p2.) | 0.45 | 0.06 | [0.33, 0.56] | 7.57 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_Insula_run1 (.p3.) | 0.23 | 0.04 | [0.14, 0.31] | 5.29 | < .001 | 1.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 (.p4.) | 0.43 | 0.05 | [0.34, 0.52] | 9.39 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 (.p5.) | 0.40 | 0.06 | [0.28, 0.51] | 7.00 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_Insula_run1 (.p6.) | 0.26 | 0.05 | [0.17, 0.35] | 5.71 | < .001 | 1.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 (.p7.) | 0.43 | 0.05 | [0.32, 0.53] | 7.98 | < .001 | 1.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 (.p8.) | 0.45 | 0.06 | [0.35, 0.56] | 8.15 | < .001 | 1.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 (.p9.) | 0.55 | 0.06 | [0.43, 0.68] | 8.73 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 (.p10.) | 0.48 | 0.05 | [0.38, 0.58] | 9.57 | < .001 | 1.00
## Approach =~ AWin_v_Neut_R_Insula_run2 (.p11.) | 0.26 | 0.04 | [0.17, 0.35] | 5.88 | < .001 | 1.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 (.p12.) | 0.38 | 0.04 | [0.30, 0.47] | 8.57 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 (.p13.) | 0.41 | 0.06 | [0.30, 0.52] | 7.36 | < .001 | 1.00
## Approach =~ BWin_v_Neut_R_Insula_run2 (.p14.) | 0.29 | 0.04 | [0.20, 0.37] | 6.58 | < .001 | 1.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 (.p15.) | 0.38 | 0.05 | [0.28, 0.48] | 7.75 | < .001 | 1.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 (.p16.) | 0.48 | 0.05 | [0.38, 0.59] | 8.80 | < .001 | 1.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 (.p17.) | 0.46 | 0.05 | [0.36, 0.56] | 8.86 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 (.p18.) | 0.46 | 0.06 | [0.34, 0.58] | 7.53 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 (.p19.) | 0.48 | 0.06 | [0.37, 0.59] | 8.70 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 (.p20.) | 0.50 | 0.06 | [0.38, 0.61] | 8.39 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 (.p21.) | 0.26 | 0.05 | [0.16, 0.36] | 5.22 | < .001 | 1.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 (.p22.) | 0.44 | 0.05 | [0.33, 0.54] | 8.14 | < .001 | 1.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 (.p23.) | 0.41 | 0.05 | [0.31, 0.50] | 8.13 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 (.p24.) | 0.51 | 0.06 | [0.38, 0.63] | 7.80 | < .001 | 1.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 (.p25.) | 0.48 | 0.06 | [0.38, 0.59] | 8.77 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 (.p26.) | 0.41 | 0.05 | [0.30, 0.51] | 7.42 | < .001 | 1.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 (.p27.) | 0.26 | 0.05 | [0.16, 0.35] | 5.33 | < .001 | 1.00
## Approach =~ AWin_v_Neut_L_NAcc_run1 (.p1.) | 0.37 | 0.05 | [0.29, 0.46] | 8.25 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 (.p2.) | 0.40 | 0.05 | [0.31, 0.50] | 8.22 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_Insula_run1 (.p3.) | 0.21 | 0.03 | [0.15, 0.28] | 6.28 | < .001 | 2.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 (.p4.) | 0.40 | 0.04 | [0.32, 0.47] | 10.12 | < .001 | 2.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 (.p5.) | 0.35 | 0.04 | [0.26, 0.43] | 8.41 | < .001 | 2.00
## Approach =~ BWin_v_Neut_R_Insula_run1 (.p6.) | 0.20 | 0.03 | [0.14, 0.27] | 5.98 | < .001 | 2.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 (.p7.) | 0.41 | 0.05 | [0.32, 0.49] | 8.99 | < .001 | 2.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 (.p8.) | 0.44 | 0.05 | [0.35, 0.52] | 9.63 | < .001 | 2.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 (.p9.) | 0.39 | 0.04 | [0.31, 0.48] | 9.16 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 (.p10.) | 0.43 | 0.05 | [0.33, 0.52] | 8.63 | < .001 | 2.00
## Approach =~ AWin_v_Neut_R_Insula_run2 (.p11.) | 0.21 | 0.03 | [0.15, 0.28] | 6.38 | < .001 | 2.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 (.p12.) | 0.34 | 0.04 | [0.26, 0.41] | 8.59 | < .001 | 2.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 (.p13.) | 0.35 | 0.04 | [0.28, 0.43] | 9.44 | < .001 | 2.00
## Approach =~ BWin_v_Neut_R_Insula_run2 (.p14.) | 0.20 | 0.04 | [0.13, 0.27] | 5.72 | < .001 | 2.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 (.p15.) | 0.38 | 0.04 | [0.29, 0.47] | 8.52 | < .001 | 2.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 (.p16.) | 0.40 | 0.04 | [0.33, 0.48] | 10.49 | < .001 | 2.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 (.p17.) | 0.48 | 0.06 | [0.37, 0.59] | 8.71 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 (.p18.) | 0.48 | 0.05 | [0.38, 0.57] | 10.13 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 (.p19.) | 0.45 | 0.05 | [0.36, 0.55] | 9.80 | < .001 | 2.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 (.p20.) | 0.46 | 0.06 | [0.35, 0.57] | 8.21 | < .001 | 2.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 (.p21.) | 0.28 | 0.05 | [0.19, 0.37] | 6.04 | < .001 | 2.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 (.p22.) | 0.54 | 0.07 | [0.41, 0.67] | 8.23 | < .001 | 2.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 (.p23.) | 0.43 | 0.05 | [0.32, 0.53] | 7.97 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 (.p24.) | 0.52 | 0.05 | [0.43, 0.62] | 10.78 | < .001 | 2.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 (.p25.) | 0.48 | 0.05 | [0.37, 0.58] | 8.85 | < .001 | 2.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 (.p26.) | 0.48 | 0.06 | [0.36, 0.60] | 8.12 | < .001 | 2.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 (.p27.) | 0.23 | 0.04 | [0.15, 0.31] | 5.69 | < .001 | 2.00
## Approach =~ AWin_v_Neut_L_NAcc_run1 (.p1.) | 0.46 | 0.03 | [0.40, 0.52] | 14.42 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_NAcc_run1 (.p2.) | 0.44 | 0.03 | [0.39, 0.50] | 14.90 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_Insula_run1 (.p3.) | 0.24 | 0.03 | [0.17, 0.30] | 7.19 | < .001 | 3.00
## Approach =~ BWin_v_Neut_L_NAcc_run1 (.p4.) | 0.48 | 0.03 | [0.42, 0.54] | 15.98 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_NAcc_run1 (.p5.) | 0.39 | 0.03 | [0.33, 0.45] | 12.99 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_Insula_run1 (.p6.) | 0.24 | 0.03 | [0.18, 0.31] | 7.72 | < .001 | 3.00
## Approach =~ BWin_v_BLose_L_NAcc_run1 (.p7.) | 0.47 | 0.03 | [0.41, 0.52] | 15.52 | < .001 | 3.00
## Approach =~ BWin_v_BLose_R_NAcc_run1 (.p8.) | 0.47 | 0.03 | [0.41, 0.53] | 15.67 | < .001 | 3.00
## Approach =~ AWin_v_Neut_L_NAcc_run2 (.p9.) | 0.50 | 0.03 | [0.44, 0.56] | 16.89 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_NAcc_run2 (.p10.) | 0.50 | 0.03 | [0.45, 0.56] | 18.44 | < .001 | 3.00
## Approach =~ AWin_v_Neut_R_Insula_run2 (.p11.) | 0.27 | 0.03 | [0.20, 0.33] | 8.35 | < .001 | 3.00
## Approach =~ BWin_v_Neut_L_NAcc_run2 (.p12.) | 0.40 | 0.03 | [0.33, 0.46] | 12.36 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_NAcc_run2 (.p13.) | 0.43 | 0.03 | [0.37, 0.48] | 14.45 | < .001 | 3.00
## Approach =~ BWin_v_Neut_R_Insula_run2 (.p14.) | 0.25 | 0.04 | [0.18, 0.32] | 7.10 | < .001 | 3.00
## Approach =~ BWin_v_BLose_L_NAcc_run2 (.p15.) | 0.42 | 0.03 | [0.35, 0.48] | 13.48 | < .001 | 3.00
## Approach =~ BWin_v_BLose_R_NAcc_run2 (.p16.) | 0.48 | 0.03 | [0.42, 0.54] | 15.77 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_L_Insula_run1 (.p17.) | 0.46 | 0.03 | [0.39, 0.52] | 13.29 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_L_Insula_run1 (.p18.) | 0.45 | 0.03 | [0.39, 0.52] | 13.50 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_R_Insula_run1 (.p19.) | 0.50 | 0.03 | [0.43, 0.56] | 14.43 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_L_Insula_run1 (.p20.) | 0.45 | 0.03 | [0.39, 0.52] | 13.78 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_R_Insula_run1 (.p21.) | 0.24 | 0.03 | [0.18, 0.31] | 7.25 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_L_Insula_run2 (.p22.) | 0.44 | 0.03 | [0.38, 0.50] | 14.36 | < .001 | 3.00
## Avoid =~ ALose_v_Neut_R_Insula_run2 (.p23.) | 0.39 | 0.03 | [0.33, 0.45] | 13.57 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_L_Insula_run2 (.p24.) | 0.43 | 0.03 | [0.36, 0.49] | 12.43 | < .001 | 3.00
## Avoid =~ BLose_v_Neut_R_Insula_run2 (.p25.) | 0.48 | 0.03 | [0.42, 0.55] | 14.65 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_L_Insula_run2 (.p26.) | 0.44 | 0.03 | [0.37, 0.50] | 13.26 | < .001 | 3.00
## Avoid =~ BLose_v_BWin_R_Insula_run2 (.p27.) | 0.24 | 0.03 | [0.17, 0.30] | 6.94 | < .001 | 3.00
##
## # Correlation
##
## Link | Coefficient | SE | 95% CI | z | p | Group
## ---------------------------------------------------------------------------------
## Approach ~~ Avoid | -0.32 | 0.12 | [-0.55, -0.09] | -2.77 | 0.006 | 1.00
## Approach ~~ Avoid | -0.41 | 0.11 | [-0.62, -0.19] | -3.73 | < .001 | 2.00
## Approach ~~ Avoid | -0.56 | 0.03 | [-0.63, -0.50] | -16.63 | < .001 | 3.002.8 Comparing models w/ BIC/AIC (anova)
The below compares whether the complete data (across all three
samples) in the all_cfa model is significantly improved by
the configural invariance model. A significant value indicates that the
configural model is significantly better than the full sample cfa.
anova(all_cfa, configural_cfa)## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## all_cfa 323 -19878 -19459 1992.0
## configural_cfa 969 -19740 -18483 2725.1 759.78 646 0.001285 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Next, anova is used to compare the model improve in AIC/BIC by between the configural and metric invariance. A significantly result in the anova would indicate a significant improvement of the metric model over the configural model.
anova(configural_cfa, metric_cfa)## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## configural_cfa 969 -19740 -18483 2725.1
## metric_cfa 1019 -19767 -18766 2797.8 58.114 50 0.20132.9 Plotting multi-group config. CFA
Use semPaths to plot the configural invariance CFA multigroup model
# this plottinig is not function with runs loading onto ROIs
layout(t(1:3))
semPaths(configural_cfa,
color = "lightyellow",
theme="colorblind",
whatLabels = "std",
style = "lisrel",
sizeLat = 10,
sizeLat2 = 10,
sizeMan = 6,
edge.color = "steelblue",
edge.label.cex = 2,
label.cex = 2,
rotation = 2,
layout = "tree2",
intercepts = TRUE,
residuals = FALSE,
#residScale = 10,
curve = 2,
title = T,
title.color = "black",
cardinal = "lat cov",
curvePivot = T,
nCharNodes = 6,
#nodeLabels = label,
mar = c(2,5,2,6))
# Title
title("Multi-group CFA on MID task Contrasts")
3 Running [semi-restricted] ESEM Model
As described in the manuscript, the restricted CFA may incorrectly account for some measurement error in the items. This may degrade the fit statistics. See Marsh et al. (2014) for an in-depth discussion.
In this case, Exploratory Structural Equation Modeling (ESEM) is used
to fit a CFA pre-specified model that allows for non-zero loadings. The
technique and application of ESEM is available through the psych
esem and esemcomp package. Here,
the esemcomp package is used to fit a model using the steps
described by Mateus Silvestrin here and by Guà rdia-Olmos
et al.. The github code for esemcomp is available here. Below
can be used to download the esemComp package – which worked with R
version 4.2.1 on x86_64-apply-dawin17.0 during September 2022.
devtools::install_github("MateusPsi/esemComp", build_vignettes = TRUE)
3.1 Selected items for ESEM
First, select the items that are consistent with those in the CFA model
# ordering so can specify numerically
esem_data = brain_set[,c("AWin_v_Neut_L_NAcc_run1" ,"AWin_v_Neut_L_NAcc_run2" ,
"BWin_v_Neut_L_NAcc_run1" ,"BWin_v_Neut_L_NAcc_run2" ,
"BWin_v_BLose_L_NAcc_run1" ,"BWin_v_BLose_L_NAcc_run2",
"AWin_v_Neut_R_NAcc_run1" , "AWin_v_Neut_R_NAcc_run2",
"BWin_v_Neut_R_NAcc_run1" , "BWin_v_Neut_R_NAcc_run2",
"BWin_v_BLose_R_NAcc_run1", "BWin_v_BLose_R_NAcc_run2",
# insula values apprach
"AWin_v_Neut_R_Insula_run1","AWin_v_Neut_R_Insula_run2",
"BWin_v_Neut_R_Insula_run1","BWin_v_Neut_R_Insula_run2",
# avoidance
"ALose_v_Neut_L_Insula_run1","ALose_v_Neut_L_Insula_run2",
"BLose_v_Neut_L_Insula_run1","BLose_v_Neut_L_Insula_run2",
"BLose_v_BWin_L_Insula_run1","BLose_v_BWin_L_Insula_run2",
"ALose_v_Neut_R_Insula_run1","ALose_v_Neut_R_Insula_run2",
"BLose_v_Neut_R_Insula_run1","BLose_v_Neut_R_Insula_run2",
"BLose_v_BWin_R_Insula_run1","BLose_v_BWin_R_Insula_run2",
"set")]3.2 Specify EFA Model
As described in March et al. (2014), create a target rotation for
items onto factors. In this case two factors are specified by the CFA
model, so factor 1 and factor 2 are specified in
make_target.
# First, consistent w/ March et al. (2014), creating target rotation
# ensure they match onto variable list
target_rot <- make_target(28,mainloadings = list(f1 = 1:16, f2 = 17:28))
esem.efa <- esem_efa(data = esem_data[,1:28], nfactors = 2,
target = target_rot, fm = "ml")## Loading required namespace: GPArotationesem.efa$loadings##
## Loadings:
## ML1 ML2
## AWin_v_Neut_L_NAcc_run1 0.408
## AWin_v_Neut_L_NAcc_run2 0.475
## BWin_v_Neut_L_NAcc_run1 0.451
## BWin_v_Neut_L_NAcc_run2 0.384
## BWin_v_BLose_L_NAcc_run1 0.390
## BWin_v_BLose_L_NAcc_run2 0.359
## AWin_v_Neut_R_NAcc_run1 0.514 0.105
## AWin_v_Neut_R_NAcc_run2 0.561
## BWin_v_Neut_R_NAcc_run1 0.412
## BWin_v_Neut_R_NAcc_run2 0.426
## BWin_v_BLose_R_NAcc_run1 0.449
## BWin_v_BLose_R_NAcc_run2 0.493
## AWin_v_Neut_R_Insula_run1 0.216
## AWin_v_Neut_R_Insula_run2 0.253
## BWin_v_Neut_R_Insula_run1 0.237
## BWin_v_Neut_R_Insula_run2 0.207
## ALose_v_Neut_L_Insula_run1 0.480
## ALose_v_Neut_L_Insula_run2 0.477
## BLose_v_Neut_L_Insula_run1 0.433
## BLose_v_Neut_L_Insula_run2 0.423
## BLose_v_BWin_L_Insula_run1 0.457
## BLose_v_BWin_L_Insula_run2 0.454
## ALose_v_Neut_R_Insula_run1 0.475
## ALose_v_Neut_R_Insula_run2 0.435
## BLose_v_Neut_R_Insula_run1 0.454
## BLose_v_Neut_R_Insula_run2 0.454
## BLose_v_BWin_R_Insula_run1 0.207
## BLose_v_BWin_R_Insula_run2 0.198
##
## ML1 ML2
## SS loadings 2.625 2.196
## Proportion Var 0.094 0.078
## Cumulative Var 0.094 0.172Using item that loads highest on factor 1 and lowest on factor 2 and
vice versa, and define as anchor using find_referents
# per the example from Mateus Silverstrin, need to define anchor for each factor (value to loads highers on 1 factor and lowest on other)
anchor <- find_referents(efa_object = esem.efa,factor_names = c("f1","f2"))Once the esem efa and anchors are defined, use
syntax_composer to specied the esem model. This will
produce a lavaan specified model that references starting values that
will be used in the cfa model
# Pull starting parameters
esem_mid_model <- syntax_composer(efa_object = esem.efa, referents = anchor)3.3 Run ESEM model
3.3.1 Specified Model
The starting values are printed below to provide reference for how starting values differ from a strict CFA model. Notice, how some values that were original not fit onto the Approach factor (f1), such as big lose contrasts, they are now specified with loading values that are between .05 to -.05.
cat(esem_mid_model)## f1 =~ start(0.408)*AWin_v_Neut_L_NAcc_run1+
## start(0.475)*AWin_v_Neut_L_NAcc_run2+
## start(0.451)*BWin_v_Neut_L_NAcc_run1+
## start(0.384)*BWin_v_Neut_L_NAcc_run2+
## start(0.39)*BWin_v_BLose_L_NAcc_run1+
## start(0.359)*BWin_v_BLose_L_NAcc_run2+
## start(0.514)*AWin_v_Neut_R_NAcc_run1+
## start(0.561)*AWin_v_Neut_R_NAcc_run2+
## start(0.412)*BWin_v_Neut_R_NAcc_run1+
## start(0.426)*BWin_v_Neut_R_NAcc_run2+
## start(0.449)*BWin_v_BLose_R_NAcc_run1+
## start(0.493)*BWin_v_BLose_R_NAcc_run2+
## start(0.216)*AWin_v_Neut_R_Insula_run1+
## start(0.253)*AWin_v_Neut_R_Insula_run2+
## start(0.237)*BWin_v_Neut_R_Insula_run1+
## start(0.207)*BWin_v_Neut_R_Insula_run2+
## 0.056*ALose_v_Neut_L_Insula_run1+
## start(0.058)*ALose_v_Neut_L_Insula_run2+
## start(-0.017)*BLose_v_Neut_L_Insula_run1+
## start(-0.013)*BLose_v_Neut_L_Insula_run2+
## start(-0.006)*BLose_v_BWin_L_Insula_run1+
## start(0.031)*BLose_v_BWin_L_Insula_run2+
## start(-0.041)*ALose_v_Neut_R_Insula_run1+
## start(-0.044)*ALose_v_Neut_R_Insula_run2+
## start(-0.027)*BLose_v_Neut_R_Insula_run1+
## start(-0.019)*BLose_v_Neut_R_Insula_run2+
## start(-0.045)*BLose_v_BWin_R_Insula_run1+
## start(-0.049)*BLose_v_BWin_R_Insula_run2
##
## f2 =~ start(-0.052)*AWin_v_Neut_L_NAcc_run1+
## start(-0.015)*AWin_v_Neut_L_NAcc_run2+
## start(-0.013)*BWin_v_Neut_L_NAcc_run1+
## start(-0.002)*BWin_v_Neut_L_NAcc_run2+
## start(-0.099)*BWin_v_BLose_L_NAcc_run1+
## start(-0.082)*BWin_v_BLose_L_NAcc_run2+
## start(0.105)*AWin_v_Neut_R_NAcc_run1+
## 0.091*AWin_v_Neut_R_NAcc_run2+
## start(0.037)*BWin_v_Neut_R_NAcc_run1+
## start(0.008)*BWin_v_Neut_R_NAcc_run2+
## start(-0.031)*BWin_v_BLose_R_NAcc_run1+
## start(0.026)*BWin_v_BLose_R_NAcc_run2+
## start(-0.026)*AWin_v_Neut_R_Insula_run1+
## start(-0.012)*AWin_v_Neut_R_Insula_run2+
## start(-0.016)*BWin_v_Neut_R_Insula_run1+
## start(-0.077)*BWin_v_Neut_R_Insula_run2+
## start(0.48)*ALose_v_Neut_L_Insula_run1+
## start(0.477)*ALose_v_Neut_L_Insula_run2+
## start(0.433)*BLose_v_Neut_L_Insula_run1+
## start(0.423)*BLose_v_Neut_L_Insula_run2+
## start(0.457)*BLose_v_BWin_L_Insula_run1+
## start(0.454)*BLose_v_BWin_L_Insula_run2+
## start(0.475)*ALose_v_Neut_R_Insula_run1+
## start(0.435)*ALose_v_Neut_R_Insula_run2+
## start(0.454)*BLose_v_Neut_R_Insula_run1+
## start(0.454)*BLose_v_Neut_R_Insula_run2+
## start(0.207)*BLose_v_BWin_R_Insula_run1+
## start(0.198)*BLose_v_BWin_R_Insula_run23.3.2 Running full ESEM model
After the EFA loadings are extracted using a target rotation, starting values are now available. These are now used to specify a less restrictive CFA model
esem_mid_fit<- cfa(esem_mid_model, esem_data[,1:28], std.lv=TRUE, meanstructure = TRUE,
estimator = "MLR")Pull and add fit statistics to the out dataframe and
print results to see decreases in AIC/BIC
# adding values to the CFA model fit indices
out[7,2:7] <- round(data.matrix(fitmeasures(esem_mid_fit,
fit.measures = c("chisq","df","pvalue",
"rmsea", "cfi", "srmr"))),
digits=3)
out[7,8] <- round(AIC(esem_mid_fit),3)
out[7,9] <- round(BIC(esem_mid_fit),3)
out[7,1] <- c("Overall ESEM")
out <- as.data.frame(out)
out %>%
knitr::kable(
col.names = c("Model", "Chi-sq", "DF", "p value", "RMSEA", "CFI", "SRMR", "AIC", "BIC"),
caption = "Fit statistics from CFA and ESEM models",
booktabs = TRUE
)| Model | Chi-sq | DF | p value | RMSEA | CFI | SRMR | AIC | BIC |
|---|---|---|---|---|---|---|---|---|
| AHRB CFA | 499.052 | 323 | 0 | 0.072 | 0.595 | 0.096 | -1650.715 | -1433.875 |
| MLS CFA | 525.874 | 323 | 0 | 0.072 | 0.565 | 0.09 | -1714.866 | -1486.291 |
| ABCD CFA | 1700.221 | 323 | 0 | 0.065 | 0.658 | 0.054 | -16374.249 | -15971.813 |
| Overall CFA | 1992.013 | 323 | 0 | 0.065 | 0.652 | 0.053 | -19877.767 | -19458.757 |
| Configg MG-CFA | 2725.148 | 969 | 0 | 0.067 | 0.644 | 0.061 | -19739.83 | -18482.8 |
| Metric MG-CFA | 2797.802 | 1019 | 0 | 0.065 | 0.639 | 0.063 | -19767.175 | -18765.639 |
| Overall ESEM | 2116.474 | 323 | 0 | 0.067 | 0.653 | 0.052 | -20679.389 | -20112.192 |
4 Running EFA [Unrestricted] model
Here, a data-driven exploratory factor analysis is performed as implemented using the (https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/factanal)[https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/factanal] in the stats package. The same variables as in the CFA and ESEM dataset are used.
4.1 Rec. # Factors
Taking a simple scree plot approach using the nFactors package see the recommended factors for the EFA model
fa_data <- subset(esem_data[,1:28])
par(mfrow=c(1,1))
fa.parallel(fa_data) # https://cran.r-project.org/web/packages/nFactors/nFactors.pdf
## Parallel analysis suggests that the number of factors = 14 and the number of components = 9Comparing the above with the BIC comparison of an EFA model to determine the best fitting model based on fit statistics. Factor Analysis is submitted across a range of factors, e.g., 1-5, and the BIC is extracted from the model to determine the optimal number of factors
rec_factors <- matrix(NA, ncol = 2, nrow = 20)
colnames(rec_factors) <- c("Nfactors","BIC")
for (f in 1:20) {
test_fac <- fa(r = esem_data[,1:28], #raw data
nfactors = f,
rotate = "promax")
rec_factors[f,1] <- f
rec_factors[f,2] <-test_fac$BIC
}
bic_fact = as.data.frame(rec_factors)lowest_bic <- which.min(bic_fact$BIC)
bic_fact %>%
ggplot(aes(x = Nfactors, y = BIC)) +
geom_line(colour = 'black', linetype = 'dashed') +
geom_vline(xintercept = bic_fact$Nfactors[lowest_bic], colour = 'red')+
theme_minimal()
Complementing the simple scree pot and BIC to avoid biasing of
recommendation factors that depend on strong correlations between
bilateral regions by using parallel analysis.
Parallel analysis is implemented using the paran package.
Converging information is used to identify the optimal factors.
paran(x = esem_data[,1:28],
iterations = 2000, centile = 90, quietly = FALSE,
status = TRUE, all = TRUE, cfa = TRUE, graph = TRUE, color = TRUE,
col = c("black", "red", "blue"), lty = c(1, 2, 3), lwd = 1, legend = TRUE,
#file = "", width = 640, height = 640, grdevice = "png",
seed = 111)##
## Using eigendecomposition of correlation matrix.
## Computing: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
##
##
## Results of Horn's Parallel Analysis for factor retention
## 2000 iterations, using the 90 centile estimate
##
## --------------------------------------------------
## Factor Adjusted Unadjusted Estimated
## Eigenvalue Eigenvalue Bias
## --------------------------------------------------
## No components passed.
## --------------------------------------------------
## 1 3.437425 3.779355 0.341930
## 2 0.882779 1.179309 0.296530
## 3 0.326962 0.588390 0.261427
## 4 0.280226 0.513616 0.233389
## 5 0.286416 0.494226 0.207810
## 6 0.281301 0.465349 0.184047
## 7 0.256789 0.419734 0.162945
## 8 0.251962 0.394841 0.142879
## 9 0.222816 0.345784 0.122967
## 10 0.222248 0.327018 0.104769
## 11 0.180291 0.265699 0.085408
## 12 0.179946 0.248247 0.068300
## 13 0.180402 0.231484 0.051082
## 14 0.151434 0.185454 0.034019
## 15 -0.127279 -0.11020 0.017078
## 16 -0.137681 -0.13682 0.000860
## 17 -0.124851 -0.14096 -0.01611
## 18 -0.127005 -0.15969 -0.03268
## 19 -0.124232 -0.17290 -0.04866
## 20 -0.114268 -0.17880 -0.06453
## 21 -0.121058 -0.20152 -0.08046
## 22 -0.108396 -0.20528 -0.09689
## 23 -0.115536 -0.22874 -0.11321
## 24 -0.115498 -0.24604 -0.13055
## 25 -0.102630 -0.25084 -0.14821
## 26 -0.090550 -0.25765 -0.16710
## 27 -0.087320 -0.27431 -0.18698
## 28 -0.078230 -0.28927 -0.21104
## --------------------------------------------------
##
## Adjusted eigenvalues > 0 indicate dimensions to retain.
## (14 factors retained)
4.2 Run EFA on all data
Used the (factanal)[https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/factanal]
to run EFA model. Specifying the number of factors and using the
promax (non-orthogonal) rotation.
MID_efa <- factanal(x = esem_data[,1:28], #raw data
factors = 2, rotation = "promax" # oblique rotation allow for non-orthogonal structure
)4.3 Plot EFA, Dimensions
Plot the loadings across the dimensions as represented in the affective circumplex framework.
factor.plot(ic.results = MID_efa$loadings,
labels = colnames(fa_data),
cex = .6, jiggle = FALSE,
ylim = c(-1,1), xlim = c(-1,1),
title = "Mutldimensional Plot of FA1 v FA2 Loadings"
)
4.4 Plot Factors Alt.
Plot factor loadings with respect to other factors.
pairs(MID_efa$loadings, col=1:ncol(fa_data), upper.panel=NULL, main="Factor loadings")
par(xpd=TRUE)
legend('topright', bty='n', pch='o',
col=1:ncol(fa_data), ncol = 3,
attr(MID_efa$loadings, 'dimnames')[[1]],
title="Contrasts",
cex = .4)
4.5 Heatmap of factor loadings
Create a heatmap of loadings onto the provided factors.
heatmaply(round(MID_efa$loadings[,1:2],2) %>% print(sort = T),
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "darkred",
space = "Lab",
midpoint = 0,
limits = c(-1, 1)
),
dendrogram = "none",
xlab = "", ylab = "",
main = "",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.00001,
titleX = FALSE,
hide_colorbar = FALSE,
branches_lwd = 0.1,
label_names = c("Brain:", "Feature:", "Value"),
fontsize_row = 9, fontsize_col = 9,
labCol = colnames(MID_efa$loadings[,1:2]),
labRow = rownames(MID_efa$loadings[,1:2]),
heatmap_layers = theme(axis.line=element_blank()),
)## Factor1 Factor2
## AWin_v_Neut_L_NAcc_run1 0.41 -0.05
## AWin_v_Neut_L_NAcc_run2 0.47 -0.02
## BWin_v_Neut_L_NAcc_run1 0.45 -0.01
## BWin_v_Neut_L_NAcc_run2 0.38 0.00
## BWin_v_BLose_L_NAcc_run1 0.39 -0.10
## BWin_v_BLose_L_NAcc_run2 0.36 -0.08
## AWin_v_Neut_R_NAcc_run1 0.52 0.11
## AWin_v_Neut_R_NAcc_run2 0.56 0.09
## BWin_v_Neut_R_NAcc_run1 0.41 0.04
## BWin_v_Neut_R_NAcc_run2 0.43 0.01
## BWin_v_BLose_R_NAcc_run1 0.45 -0.03
## BWin_v_BLose_R_NAcc_run2 0.49 0.03
## AWin_v_Neut_R_Insula_run1 0.21 -0.03
## AWin_v_Neut_R_Insula_run2 0.25 -0.01
## BWin_v_Neut_R_Insula_run1 0.24 -0.02
## BWin_v_Neut_R_Insula_run2 0.20 -0.08
## ALose_v_Neut_L_Insula_run1 0.07 0.49
## ALose_v_Neut_L_Insula_run2 0.08 0.49
## BLose_v_Neut_L_Insula_run1 0.00 0.44
## BLose_v_Neut_L_Insula_run2 0.00 0.43
## BLose_v_BWin_L_Insula_run1 0.01 0.47
## BLose_v_BWin_L_Insula_run2 0.05 0.46
## ALose_v_Neut_R_Insula_run1 -0.02 0.48
## ALose_v_Neut_R_Insula_run2 -0.03 0.44
## BLose_v_Neut_R_Insula_run1 -0.01 0.46
## BLose_v_Neut_R_Insula_run2 0.00 0.46
## BLose_v_BWin_R_Insula_run1 -0.04 0.21
## BLose_v_BWin_R_Insula_run2 -0.04 0.204.5.1 Run EFA on sample specific
4.5.1.1 EFA ABCD
abcd_efadata = subset(esem_data %>% filter(set==1))
abcd_efa <- factanal(x = abcd_efadata[,1:28], #raw data
factors = 2, rotation = "promax" # oblique rotation allow for non-orthogonal structure
)heatmaply(round(abcd_efa$loadings[,1:2],2) %>% print(sort = T),
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "darkred",
space = "Lab",
midpoint = 0,
limits = c(-1, 1)
),
dendrogram = "none",
xlab = "", ylab = "",
main = "",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.00001,
titleX = FALSE,
hide_colorbar = FALSE,
branches_lwd = 0.1,
label_names = c("Brain:", "Feature:", "Value"),
fontsize_row = 9, fontsize_col = 9,
labCol = colnames(abcd_efa$loadings[,1:2]),
labRow = rownames(abcd_efa$loadings[,1:2]),
heatmap_layers = theme(axis.line=element_blank()),
)## Factor1 Factor2
## AWin_v_Neut_L_NAcc_run1 0.40 -0.06
## AWin_v_Neut_L_NAcc_run2 0.45 -0.04
## BWin_v_Neut_L_NAcc_run1 0.47 -0.04
## BWin_v_Neut_L_NAcc_run2 0.41 -0.01
## BWin_v_BLose_L_NAcc_run1 0.42 -0.07
## BWin_v_BLose_L_NAcc_run2 0.37 -0.07
## AWin_v_Neut_R_NAcc_run1 0.54 0.12
## AWin_v_Neut_R_NAcc_run2 0.61 0.14
## BWin_v_Neut_R_NAcc_run1 0.39 0.01
## BWin_v_Neut_R_NAcc_run2 0.41 -0.01
## BWin_v_BLose_R_NAcc_run1 0.44 -0.04
## BWin_v_BLose_R_NAcc_run2 0.49 0.00
## AWin_v_Neut_R_Insula_run1 0.21 -0.04
## AWin_v_Neut_R_Insula_run2 0.25 -0.03
## BWin_v_Neut_R_Insula_run1 0.23 -0.01
## BWin_v_Neut_R_Insula_run2 0.22 -0.08
## ALose_v_Neut_L_Insula_run1 0.03 0.46
## ALose_v_Neut_L_Insula_run2 0.03 0.42
## BLose_v_Neut_L_Insula_run1 0.05 0.48
## BLose_v_Neut_L_Insula_run2 0.05 0.46
## BLose_v_BWin_L_Insula_run1 0.03 0.46
## BLose_v_BWin_L_Insula_run2 0.02 0.43
## ALose_v_Neut_R_Insula_run1 0.00 0.50
## ALose_v_Neut_R_Insula_run2 -0.01 0.44
## BLose_v_Neut_R_Insula_run1 0.02 0.51
## BLose_v_Neut_R_Insula_run2 0.04 0.50
## BLose_v_BWin_R_Insula_run1 -0.06 0.18
## BLose_v_BWin_R_Insula_run2 -0.06 0.184.5.1.2 EFA MLS
mls_efadata = subset(esem_data %>% filter(set==2))
mls_efa <- factanal(x = mls_efadata[,1:28], #raw data
factors = 2, rotation = "promax" # oblique rotation allow for non-orthogonal structure
)heatmaply(round(mls_efa$loadings[,1:2],2) %>% print(sort = T),
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "darkred",
space = "Lab",
midpoint = 0,
limits = c(-1, 1)
),
dendrogram = "none",
xlab = "", ylab = "",
main = "",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.00001,
titleX = FALSE,
hide_colorbar = FALSE,
branches_lwd = 0.1,
label_names = c("Brain:", "Feature:", "Value"),
fontsize_row = 9, fontsize_col = 9,
labCol = colnames(mls_efa$loadings[,1:2]),
labRow = rownames(mls_efa$loadings[,1:2]),
heatmap_layers = theme(axis.line=element_blank()),
)## Factor1 Factor2
## AWin_v_Neut_L_NAcc_run1 0.44 -0.11
## AWin_v_Neut_L_NAcc_run2 0.49 0.09
## BWin_v_Neut_L_NAcc_run1 0.36 0.27
## BWin_v_Neut_L_NAcc_run2 0.30 0.06
## BWin_v_BLose_L_NAcc_run1 0.41 0.00
## BWin_v_BLose_L_NAcc_run2 0.48 0.12
## AWin_v_Neut_R_NAcc_run1 0.47 0.12
## AWin_v_Neut_R_NAcc_run2 0.56 0.04
## BWin_v_Neut_R_NAcc_run1 0.37 0.11
## BWin_v_Neut_R_NAcc_run2 0.36 0.05
## BWin_v_BLose_R_NAcc_run1 0.44 0.02
## BWin_v_BLose_R_NAcc_run2 0.28 0.13
## AWin_v_Neut_R_Insula_run1 0.08 0.07
## AWin_v_Neut_R_Insula_run2 0.14 0.20
## BWin_v_Neut_R_Insula_run1 0.27 -0.01
## BWin_v_Neut_R_Insula_run2 0.19 0.03
## ALose_v_Neut_L_Insula_run1 0.12 0.74
## ALose_v_Neut_L_Insula_run2 0.12 0.90
## BLose_v_Neut_L_Insula_run1 -0.25 0.16
## BLose_v_Neut_L_Insula_run2 -0.25 0.22
## BLose_v_BWin_L_Insula_run1 -0.21 0.36
## BLose_v_BWin_L_Insula_run2 -0.13 0.45
## ALose_v_Neut_R_Insula_run1 -0.18 0.39
## ALose_v_Neut_R_Insula_run2 -0.30 0.27
## BLose_v_Neut_R_Insula_run1 -0.09 0.19
## BLose_v_Neut_R_Insula_run2 -0.14 0.32
## BLose_v_BWin_R_Insula_run1 0.07 0.26
## BLose_v_BWin_R_Insula_run2 0.10 0.294.5.1.3 EFA AHRB
ahrb_efadata = subset(esem_data %>% filter(set==3))
ahrb_efa <- factanal(x = ahrb_efadata[,1:28], #raw data
factors = 2, rotation = "promax" # oblique rotation allow for non-orthogonal structure
)heatmaply(round(ahrb_efa$loadings[,1:2],2) %>% print(sort = T),
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "darkred",
space = "Lab",
midpoint = 0,
limits = c(-1, 1)
),
dendrogram = "none",
xlab = "", ylab = "",
main = "",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.00001,
titleX = FALSE,
hide_colorbar = FALSE,
branches_lwd = 0.1,
label_names = c("Brain:", "Feature:", "Value"),
fontsize_row = 9, fontsize_col = 9,
labCol = colnames(ahrb_efa$loadings[,1:2]),
labRow = rownames(ahrb_efa$loadings[,1:2]),
heatmap_layers = theme(axis.line=element_blank()),
)## Factor1 Factor2
## AWin_v_Neut_L_NAcc_run1 0.61 0.22
## AWin_v_Neut_L_NAcc_run2 0.78 0.14
## BWin_v_Neut_L_NAcc_run1 0.28 -0.03
## BWin_v_Neut_L_NAcc_run2 0.18 0.00
## BWin_v_BLose_L_NAcc_run1 0.15 -0.34
## BWin_v_BLose_L_NAcc_run2 0.18 -0.24
## AWin_v_Neut_R_NAcc_run1 0.41 -0.03
## AWin_v_Neut_R_NAcc_run2 0.36 -0.10
## BWin_v_Neut_R_NAcc_run1 0.58 0.16
## BWin_v_Neut_R_NAcc_run2 0.61 0.11
## BWin_v_BLose_R_NAcc_run1 0.40 -0.09
## BWin_v_BLose_R_NAcc_run2 0.47 -0.10
## AWin_v_Neut_R_Insula_run1 0.28 -0.12
## AWin_v_Neut_R_Insula_run2 0.23 -0.18
## BWin_v_Neut_R_Insula_run1 0.18 -0.16
## BWin_v_Neut_R_Insula_run2 0.01 -0.19
## ALose_v_Neut_L_Insula_run1 0.39 0.49
## ALose_v_Neut_L_Insula_run2 0.22 0.42
## BLose_v_Neut_L_Insula_run1 0.09 0.71
## BLose_v_Neut_L_Insula_run2 0.15 0.69
## BLose_v_BWin_L_Insula_run1 -0.11 0.39
## BLose_v_BWin_L_Insula_run2 0.15 0.36
## ALose_v_Neut_R_Insula_run1 -0.10 0.33
## ALose_v_Neut_R_Insula_run2 0.10 0.50
## BLose_v_Neut_R_Insula_run1 0.00 0.44
## BLose_v_Neut_R_Insula_run2 -0.18 0.31
## BLose_v_BWin_R_Insula_run1 -0.12 0.22
## BLose_v_BWin_R_Insula_run2 -0.06 0.234.6 Comparing EFA models
Calculating a coefficient of factor congruence across the three sample’s EFA models. Using function fa.congruence
fa.congruence(x = list(abcd_efa, mls_efa, ahrb_efa), digits = 2) %>%
knitr::kable(
col.names = c("1. ABCD F1", "2. ABCD F2", "3. MLS F1", "4. MLS F2","5. AHRB F1", "6. AHRB F2"),
caption = "ABCD, MLS and AHRB EFA Factor Congruence",
booktabs = TRUE
)| 1. ABCD F1 | 2. ABCD F2 | 3. MLS F1 | 4. MLS F2 | 5. AHRB F1 | 6. AHRB F2 | |
|---|---|---|---|---|---|---|
| Factor1 | 1.00 | 0.03 | 0.89 | 0.21 | 0.85 | -0.07 |
| Factor2 | 0.03 | 1.00 | -0.25 | 0.80 | 0.09 | 0.89 |
| Factor1 | 0.89 | -0.25 | 1.00 | 0.07 | 0.78 | -0.32 |
| Factor2 | 0.21 | 0.80 | 0.07 | 1.00 | 0.31 | 0.67 |
| Factor1 | 0.85 | 0.09 | 0.78 | 0.31 | 1.00 | 0.15 |
| Factor2 | -0.07 | 0.89 | -0.32 | 0.67 | 0.15 | 1.00 |
5 Running Local SEM
Running CFA for the pubertal variables in the ABCD sample using the local SEM framework described in Olaru et al (2020) implemented using the sirt package
5.1 Run LSEM
Specifying the model for the ABCD data below. For now, using the CFA model. In future [real data] implementation, will apply the EFA CFA from n = 1000 ABCD sample in the held out n = 1000 ABCD sample. To pilot, simulating a [fake] pubertal variable that is 1 to 5, as is expected in the Pubertal Developmental Scale.
#first adding random PDS variable
sim_ABCD_data$PDS <- as.integer(rtnorm(n=1000, mean = 3.5, sd = 1.5,
lower = 1, upper = 5))
lsem.MID <- sirt::lsem.estimate(data = sim_ABCD_data, moderator = 'PDS', # moderator variable
moderator.grid = seq(1,5,1), # moderator levels, PDS 1 - 5
lavmodel = MID_model, # model
h = 2, # bandwidth parameter
residualize = FALSE, # allow mean level differences
meanstructure = TRUE,
std.lv=TRUE
)## ** Fit lavaan model
## |*****|
## |-----|
## ** Parameter summary5.2 Summary LSEM
Summarizing output of the lsem.estimate
summary(lsem.MID)## -----------------------------------------------------------------
## Local Structural Equation Model
##
## sirt 3.12-66 (2022-05-16 12:27:54)
## lavaan 0.6-12 (2022-07-04 16:40:02 UTC)
##
## R version 4.2.1 (2022-06-23) x86_64, darwin17.0 | nodename=Michaels-MacBook-Pro.local | login=root
##
## Function 'sirt::lsem.estimate', type='LSEM'
##
##
## Call:
## sirt::lsem.estimate(data = sim_ABCD_data, moderator = "PDS",
## moderator.grid = seq(1, 5, 1), lavmodel = MID_model, h = 2,
## residualize = FALSE, meanstructure = TRUE, std.lv = TRUE)
##
## Date of Analysis: 2022-10-29 13:17:00
## Time difference of 6.247428 secs
## Computation Time: 6.247428
##
## Number of observations in datasets = 1000
## Used observations in analysis = 1000
## Used sampling weights: FALSE
## Bandwidth factor = 2
## Bandwidth = 0.507
## Number of focal points for moderator = 5
##
## Used joint estimation: FALSE
## Used sufficient statistics: FALSE
## Used local linear smoothing: FALSE
## Used pseudo weights: FALSE
## Used lavaan package: TRUE
## Used lavaan.survey package: FALSE
##
## Mean structure modelled: TRUE
##
## lavaan Model
##
##
## # Factor loadings
## Approach =~ AWin_v_Neut_L_NAcc_run1 + AWin_v_Neut_R_NAcc_run1 + AWin_v_Neut_R_Insula_run1 +
## BWin_v_Neut_L_NAcc_run1 + BWin_v_Neut_R_NAcc_run1 + BWin_v_Neut_R_Insula_run1 +
## BWin_v_BLose_L_NAcc_run1 + BWin_v_BLose_R_NAcc_run1 +
## AWin_v_Neut_L_NAcc_run2 + AWin_v_Neut_R_NAcc_run2 + AWin_v_Neut_R_Insula_run2 +
## BWin_v_Neut_L_NAcc_run2 + BWin_v_Neut_R_NAcc_run2 + BWin_v_Neut_R_Insula_run2 +
## BWin_v_BLose_L_NAcc_run2 + BWin_v_BLose_R_NAcc_run2
##
## Avoid =~ ALose_v_Neut_L_Insula_run1 + ALose_v_Neut_L_Insula_run1 +
## BLose_v_Neut_L_Insula_run1 + BLose_v_Neut_R_Insula_run1 +
## BLose_v_BWin_L_Insula_run1 + BLose_v_BWin_R_Insula_run1 +
## ALose_v_Neut_L_Insula_run2 + ALose_v_Neut_R_Insula_run2 +
## BLose_v_Neut_L_Insula_run2 + BLose_v_Neut_R_Insula_run2 +
## BLose_v_BWin_L_Insula_run2 + BLose_v_BWin_R_Insula_run2
##
##
## Parameter Estimate Summary
##
## par parindex M SD
## 1 Approach=~AWin_v_Neut_L_NAcc_run1 1 0.083 0.016
## 2 Approach=~AWin_v_Neut_R_NAcc_run1 2 0.086 0.010
## 3 Approach=~AWin_v_Neut_R_Insula_run1 3 0.042 0.007
## 4 Approach=~BWin_v_Neut_L_NAcc_run1 4 0.092 0.009
## 5 Approach=~BWin_v_Neut_R_NAcc_run1 5 0.070 0.013
## 6 Approach=~BWin_v_Neut_R_Insula_run1 6 0.042 0.009
## 7 Approach=~BWin_v_BLose_L_NAcc_run1 7 0.090 0.010
## 8 Approach=~BWin_v_BLose_R_NAcc_run1 8 0.089 0.009
## 9 Approach=~AWin_v_Neut_L_NAcc_run2 9 0.089 0.011
## 10 Approach=~AWin_v_Neut_R_NAcc_run2 10 0.096 0.013
## 11 Approach=~AWin_v_Neut_R_Insula_run2 11 0.048 0.005
## 12 Approach=~BWin_v_Neut_L_NAcc_run2 12 0.080 0.006
## 13 Approach=~BWin_v_Neut_R_NAcc_run2 13 0.078 0.006
## 14 Approach=~BWin_v_Neut_R_Insula_run2 14 0.048 0.011
## 15 Approach=~BWin_v_BLose_L_NAcc_run2 15 0.077 0.005
## 16 Approach=~BWin_v_BLose_R_NAcc_run2 16 0.092 0.014
## 17 Avoid=~ALose_v_Neut_L_Insula_run1 17 0.088 0.010
## 18 Avoid=~BLose_v_Neut_L_Insula_run1 18 0.086 0.009
## 19 Avoid=~BLose_v_Neut_R_Insula_run1 19 0.097 0.010
## 20 Avoid=~BLose_v_BWin_L_Insula_run1 20 0.086 0.010
## 21 Avoid=~BLose_v_BWin_R_Insula_run1 21 0.044 0.006
## 22 Avoid=~ALose_v_Neut_L_Insula_run2 22 0.077 0.008
## 23 Avoid=~ALose_v_Neut_R_Insula_run2 23 0.075 0.008
## 24 Avoid=~BLose_v_Neut_L_Insula_run2 24 0.082 0.009
## 25 Avoid=~BLose_v_Neut_R_Insula_run2 25 0.095 0.005
## 26 Avoid=~BLose_v_BWin_L_Insula_run2 26 0.080 0.006
## 27 Avoid=~BLose_v_BWin_R_Insula_run2 27 0.041 0.006
## 28 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 0.028 0.000
## 29 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 0.031 0.004
## 30 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 0.029 0.001
## 31 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 0.025 0.002
## 32 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 0.029 0.001
## 33 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 0.031 0.002
## 34 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 0.029 0.002
## 35 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 0.029 0.001
## 36 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 0.027 0.001
## 37 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 0.028 0.001
## 38 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 0.028 0.002
## 39 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 0.030 0.002
## 40 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 0.030 0.002
## 41 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 0.031 0.002
## 42 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 0.028 0.001
## 43 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 0.028 0.001
## 44 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 0.029 0.002
## 45 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 0.028 0.002
## 46 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 0.025 0.003
## 47 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 0.030 0.001
## 48 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 0.030 0.001
## 49 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 0.029 0.002
## 50 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 0.032 0.002
## 51 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 0.030 0.002
## 52 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 0.028 0.001
## 53 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 0.029 0.002
## 54 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 0.029 0.000
## 55 Approach~~Approach 55 1.000 0.000
## 56 Avoid~~Avoid 56 1.000 0.000
## 57 Approach~~Avoid 57 -0.564 0.043
## 58 AWin_v_Neut_L_NAcc_run1~1 58 -0.003 0.008
## 59 AWin_v_Neut_R_NAcc_run1~1 59 -0.005 0.007
## 60 AWin_v_Neut_R_Insula_run1~1 60 0.008 0.003
## 61 BWin_v_Neut_L_NAcc_run1~1 61 -0.010 0.012
## 62 BWin_v_Neut_R_NAcc_run1~1 62 -0.005 0.006
## 63 BWin_v_Neut_R_Insula_run1~1 63 0.013 0.005
## 64 BWin_v_BLose_L_NAcc_run1~1 64 -0.001 0.008
## 65 BWin_v_BLose_R_NAcc_run1~1 65 -0.003 0.003
## 66 AWin_v_Neut_L_NAcc_run2~1 66 -0.004 0.008
## 67 AWin_v_Neut_R_NAcc_run2~1 67 0.002 0.009
## 68 AWin_v_Neut_R_Insula_run2~1 68 0.006 0.006
## 69 BWin_v_Neut_L_NAcc_run2~1 69 -0.004 0.011
## 70 BWin_v_Neut_R_NAcc_run2~1 70 0.000 0.009
## 71 BWin_v_Neut_R_Insula_run2~1 71 -0.001 0.001
## 72 BWin_v_BLose_L_NAcc_run2~1 72 -0.007 0.007
## 73 BWin_v_BLose_R_NAcc_run2~1 73 -0.006 0.006
## 74 ALose_v_Neut_L_Insula_run1~1 74 -0.004 0.006
## 75 BLose_v_Neut_L_Insula_run1~1 75 0.004 0.001
## 76 BLose_v_Neut_R_Insula_run1~1 76 0.015 0.006
## 77 BLose_v_BWin_L_Insula_run1~1 77 0.008 0.010
## 78 BLose_v_BWin_R_Insula_run1~1 78 0.003 0.010
## 79 ALose_v_Neut_L_Insula_run2~1 79 -0.001 0.001
## 80 ALose_v_Neut_R_Insula_run2~1 80 0.001 0.008
## 81 BLose_v_Neut_L_Insula_run2~1 81 0.011 0.009
## 82 BLose_v_Neut_R_Insula_run2~1 82 0.005 0.007
## 83 BLose_v_BWin_L_Insula_run2~1 83 0.005 0.007
## 84 BLose_v_BWin_R_Insula_run2~1 84 -0.004 0.012
## 85 Approach~1 85 0.000 0.000
## 86 Avoid~1 86 0.000 0.000
## 87 rmsea 87 0.078 0.006
## 88 cfi 88 0.578 0.042
## 89 tli 89 0.541 0.046
## 90 gfi 90 0.857 0.015
## 91 srmr 91 0.063 0.004
## MAD Min Max lin_int lin_slo SD_nonlin
## 1 0.014 0.065 0.106 0.043 0.015 0.005
## 2 0.007 0.080 0.110 0.104 -0.007 0.007
## 3 0.006 0.035 0.051 0.042 0.000 0.007
## 4 0.008 0.084 0.108 0.083 0.004 0.008
## 5 0.011 0.047 0.092 0.043 0.010 0.009
## 6 0.008 0.030 0.057 0.041 0.001 0.009
## 7 0.009 0.076 0.101 0.069 0.008 0.006
## 8 0.009 0.081 0.104 0.081 0.003 0.009
## 9 0.010 0.075 0.105 0.062 0.010 0.005
## 10 0.012 0.079 0.113 0.084 0.004 0.013
## 11 0.004 0.043 0.055 0.052 -0.002 0.004
## 12 0.005 0.073 0.088 0.076 0.002 0.006
## 13 0.006 0.069 0.089 0.067 0.004 0.005
## 14 0.011 0.038 0.066 0.043 0.002 0.011
## 15 0.005 0.071 0.086 0.075 0.001 0.005
## 16 0.013 0.065 0.106 0.060 0.012 0.008
## 17 0.009 0.080 0.100 0.110 -0.008 0.005
## 18 0.008 0.072 0.096 0.075 0.004 0.009
## 19 0.010 0.079 0.105 0.106 -0.003 0.009
## 20 0.009 0.075 0.104 0.091 -0.002 0.010
## 21 0.006 0.035 0.049 0.033 0.004 0.004
## 22 0.007 0.066 0.089 0.089 -0.005 0.006
## 23 0.006 0.070 0.095 0.090 -0.006 0.006
## 24 0.008 0.067 0.091 0.074 0.003 0.009
## 25 0.004 0.089 0.102 0.100 -0.002 0.005
## 26 0.004 0.074 0.094 0.094 -0.005 0.003
## 27 0.006 0.034 0.048 0.030 0.004 0.005
## 28 0.000 0.027 0.028 0.027 0.000 0.000
## 29 0.003 0.024 0.036 0.022 0.003 0.001
## 30 0.001 0.027 0.030 0.028 0.001 0.001
## 31 0.002 0.023 0.028 0.028 -0.001 0.002
## 32 0.001 0.027 0.030 0.029 0.000 0.001
## 33 0.002 0.025 0.032 0.026 0.002 0.002
## 34 0.002 0.025 0.032 0.028 0.000 0.002
## 35 0.001 0.026 0.030 0.027 0.001 0.001
## 36 0.001 0.026 0.029 0.028 -0.001 0.001
## 37 0.001 0.027 0.030 0.028 0.000 0.001
## 38 0.002 0.027 0.032 0.024 0.001 0.001
## 39 0.001 0.025 0.032 0.026 0.001 0.001
## 40 0.002 0.028 0.031 0.031 0.000 0.002
## 41 0.002 0.025 0.033 0.027 0.001 0.002
## 42 0.001 0.026 0.029 0.027 0.000 0.001
## 43 0.001 0.027 0.031 0.026 0.001 0.001
## 44 0.002 0.026 0.032 0.028 0.000 0.002
## 45 0.001 0.025 0.031 0.028 0.000 0.002
## 46 0.002 0.021 0.028 0.020 0.002 0.002
## 47 0.001 0.029 0.031 0.029 0.000 0.001
## 48 0.001 0.029 0.031 0.032 0.000 0.001
## 49 0.001 0.026 0.032 0.029 0.000 0.002
## 50 0.001 0.029 0.034 0.036 -0.001 0.001
## 51 0.002 0.028 0.034 0.033 -0.001 0.002
## 52 0.000 0.027 0.029 0.028 0.000 0.001
## 53 0.002 0.027 0.032 0.035 -0.002 0.001
## 54 0.000 0.028 0.030 0.030 0.000 0.000
## 55 0.000 1.000 1.000 1.000 0.000 0.000
## 56 0.000 1.000 1.000 1.000 0.000 0.000
## 57 0.029 -0.597 -0.462 -0.486 -0.029 0.031
## 58 0.007 -0.012 0.010 0.003 -0.002 0.007
## 59 0.005 -0.023 -0.001 -0.016 0.004 0.006
## 60 0.002 0.004 0.012 0.004 0.001 0.003
## 61 0.011 -0.026 0.004 -0.034 0.009 0.008
## 62 0.005 -0.016 0.003 0.000 -0.002 0.006
## 63 0.004 0.006 0.020 0.004 0.003 0.003
## 64 0.007 -0.019 0.006 0.017 -0.007 0.005
## 65 0.002 -0.007 0.004 -0.001 -0.001 0.003
## 66 0.007 -0.016 0.008 -0.001 -0.001 0.008
## 67 0.007 -0.019 0.010 -0.017 0.007 0.006
## 68 0.005 -0.002 0.013 0.004 0.001 0.006
## 69 0.010 -0.016 0.011 -0.021 0.007 0.009
## 70 0.007 -0.010 0.011 -0.014 0.005 0.007
## 71 0.001 -0.003 0.000 -0.003 0.001 0.000
## 72 0.006 -0.020 0.001 0.001 -0.003 0.006
## 73 0.005 -0.017 0.003 0.006 -0.004 0.003
## 74 0.005 -0.009 0.005 0.005 -0.003 0.005
## 75 0.001 0.003 0.006 0.003 0.000 0.001
## 76 0.005 0.006 0.025 0.017 0.000 0.006
## 77 0.009 -0.008 0.021 0.007 0.000 0.010
## 78 0.009 -0.005 0.019 0.016 -0.005 0.009
## 79 0.001 -0.002 0.001 0.001 -0.001 0.001
## 80 0.007 -0.005 0.018 -0.015 0.006 0.005
## 81 0.007 -0.002 0.027 -0.010 0.008 0.003
## 82 0.007 -0.003 0.014 -0.001 0.002 0.006
## 83 0.007 -0.003 0.017 0.008 -0.001 0.007
## 84 0.011 -0.014 0.019 0.026 -0.011 0.005
## 85 0.000 0.000 0.000 0.000 0.000 0.000
## 86 0.000 0.000 0.000 0.000 0.000 0.000
## 87 0.004 0.073 0.090 0.089 -0.004 0.004
## 88 0.040 0.528 0.627 0.473 0.039 0.016
## 89 0.044 0.488 0.594 0.428 0.042 0.017
## 90 0.011 0.824 0.871 0.827 0.011 0.010
## 91 0.003 0.061 0.072 0.073 -0.003 0.002
##
## Distribution of Moderator: Density and Effective Sample Size
##
## M=2.702 | SD=1.009
##
## moderator wgt Neff
## 1 1 0.141 180.999
## 2 2 0.279 344.553
## 3 3 0.317 394.505
## 4 4 0.263 308.413
## 5 5 0.000 37.712
##
## variable M SD min max
## 1 moderator 2.702 1.009 1.000 4.000
## 2 wgt 0.200 0.130 0.000 0.317
## 3 Neff 253.236 144.056 37.712 394.5055.3 Plot LSEM
Plotting the lsem.estimate for the first 20 indexes.
plot(lsem.MID, parindex=1:20)
5.4 Permutation Test LSEM
Running permutation test of LSEM model. In this case, using 10 permutation to save on time. In future iterations, permutations will be 1000.
lsem.permuted <- sirt::lsem.permutationTest(lsem.object = lsem.MID,
B = 10, # permutations
residualize = FALSE) ## Permutation test LSEM
## 1 2 3 4 5 6 7 8 9 10summary(lsem.permuted) # examine results## -----------------------------------------------------------------
## Permutation Test for Local Structural Equation Model
##
## sirt 3.12-66 (2022-05-16 12:27:54)
## lavaan 0.6-12 (2022-07-04 16:40:02 UTC)
##
## Function 'sirt::lsem.permutationTest'
##
##
## Call:
## sirt::lsem.permutationTest(lsem.object = lsem.MID, B = 10, residualize = FALSE)
##
## Date of Analysis: 2022-10-29 13:18:00
## Time difference of 58.0105 secs
## Computation Time: 58.0105
##
## Number of permutations = 10
## Percentage of non-converged datasets = 0
## Number of observations=1000
## Bandwidth factor=2
## Bandwidth=0.507
## Number of focal points for moderator=5
##
## lavaan Model
##
##
## # Factor loadings
## Approach =~ AWin_v_Neut_L_NAcc_run1 + AWin_v_Neut_R_NAcc_run1 + AWin_v_Neut_R_Insula_run1 +
## BWin_v_Neut_L_NAcc_run1 + BWin_v_Neut_R_NAcc_run1 + BWin_v_Neut_R_Insula_run1 +
## BWin_v_BLose_L_NAcc_run1 + BWin_v_BLose_R_NAcc_run1 +
## AWin_v_Neut_L_NAcc_run2 + AWin_v_Neut_R_NAcc_run2 + AWin_v_Neut_R_Insula_run2 +
## BWin_v_Neut_L_NAcc_run2 + BWin_v_Neut_R_NAcc_run2 + BWin_v_Neut_R_Insula_run2 +
## BWin_v_BLose_L_NAcc_run2 + BWin_v_BLose_R_NAcc_run2
##
## Avoid =~ ALose_v_Neut_L_Insula_run1 + ALose_v_Neut_L_Insula_run1 +
## BLose_v_Neut_L_Insula_run1 + BLose_v_Neut_R_Insula_run1 +
## BLose_v_BWin_L_Insula_run1 + BLose_v_BWin_R_Insula_run1 +
## ALose_v_Neut_L_Insula_run2 + ALose_v_Neut_R_Insula_run2 +
## BLose_v_Neut_L_Insula_run2 + BLose_v_Neut_R_Insula_run2 +
## BLose_v_BWin_L_Insula_run2 + BLose_v_BWin_R_Insula_run2
##
##
## Global Test Statistics
##
## par M SD SD_p
## 1 Approach=~AWin_v_Neut_L_NAcc_run1 0.083 0.016 0.1
## 2 Approach=~AWin_v_Neut_R_NAcc_run1 0.086 0.010 0.5
## 3 Approach=~AWin_v_Neut_R_Insula_run1 0.042 0.007 0.4
## 4 Approach=~BWin_v_Neut_L_NAcc_run1 0.092 0.009 0.2
## 5 Approach=~BWin_v_Neut_R_NAcc_run1 0.070 0.013 0.1
## 6 Approach=~BWin_v_Neut_R_Insula_run1 0.042 0.009 0.5
## 7 Approach=~BWin_v_BLose_L_NAcc_run1 0.090 0.010 0.1
## 8 Approach=~BWin_v_BLose_R_NAcc_run1 0.089 0.009 0.3
## 9 Approach=~AWin_v_Neut_L_NAcc_run2 0.089 0.011 0.3
## 10 Approach=~AWin_v_Neut_R_NAcc_run2 0.096 0.013 0.0
## 11 Approach=~AWin_v_Neut_R_Insula_run2 0.048 0.005 0.9
## 12 Approach=~BWin_v_Neut_L_NAcc_run2 0.080 0.006 0.6
## 13 Approach=~BWin_v_Neut_R_NAcc_run2 0.078 0.006 0.4
## 14 Approach=~BWin_v_Neut_R_Insula_run2 0.048 0.011 0.2
## 15 Approach=~BWin_v_BLose_L_NAcc_run2 0.077 0.005 0.7
## 16 Approach=~BWin_v_BLose_R_NAcc_run2 0.092 0.014 0.2
## 17 Avoid=~ALose_v_Neut_L_Insula_run1 0.088 0.010 0.5
## 18 Avoid=~BLose_v_Neut_L_Insula_run1 0.086 0.009 0.5
## 19 Avoid=~BLose_v_Neut_R_Insula_run1 0.097 0.010 0.4
## 20 Avoid=~BLose_v_BWin_L_Insula_run1 0.086 0.010 0.3
## 21 Avoid=~BLose_v_BWin_R_Insula_run1 0.044 0.006 0.6
## 22 Avoid=~ALose_v_Neut_L_Insula_run2 0.077 0.008 0.5
## 23 Avoid=~ALose_v_Neut_R_Insula_run2 0.075 0.008 0.3
## 24 Avoid=~BLose_v_Neut_L_Insula_run2 0.082 0.009 0.4
## 25 Avoid=~BLose_v_Neut_R_Insula_run2 0.095 0.005 1.0
## 26 Avoid=~BLose_v_BWin_L_Insula_run2 0.080 0.006 0.9
## 27 Avoid=~BLose_v_BWin_R_Insula_run2 0.041 0.006 0.9
## 28 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 0.028 0.000 1.0
## 29 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 0.031 0.004 0.2
## 30 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 0.029 0.001 0.6
## 31 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 0.025 0.002 0.1
## 32 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 0.029 0.001 0.9
## 33 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 0.031 0.002 0.3
## 34 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 0.029 0.002 0.1
## 35 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 0.029 0.001 0.6
## 36 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 0.027 0.001 0.8
## 37 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 0.028 0.001 0.9
## 38 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 0.028 0.002 0.3
## 39 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 0.030 0.002 0.3
## 40 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 0.030 0.002 0.5
## 41 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 0.031 0.002 0.2
## 42 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 0.028 0.001 0.9
## 43 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 0.028 0.001 0.5
## 44 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 0.029 0.002 0.2
## 45 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 0.028 0.002 0.4
## 46 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 0.025 0.003 0.1
## 47 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 0.030 0.001 1.0
## 48 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 0.030 0.001 0.9
## 49 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 0.029 0.002 0.6
## 50 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 0.032 0.002 0.4
## 51 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 0.030 0.002 0.3
## 52 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 0.028 0.001 0.9
## 53 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 0.029 0.002 0.4
## 54 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 0.029 0.000 1.0
## 55 Approach~~Approach 1.000 0.000 1.0
## 56 Avoid~~Avoid 1.000 0.000 1.0
## 57 Approach~~Avoid -0.564 0.043 0.3
## 58 AWin_v_Neut_L_NAcc_run1~1 -0.003 0.008 0.2
## 59 AWin_v_Neut_R_NAcc_run1~1 -0.005 0.007 0.3
## 60 AWin_v_Neut_R_Insula_run1~1 0.008 0.003 0.8
## 61 BWin_v_Neut_L_NAcc_run1~1 -0.010 0.012 0.0
## 62 BWin_v_Neut_R_NAcc_run1~1 -0.005 0.006 0.9
## 63 BWin_v_Neut_R_Insula_run1~1 0.013 0.005 0.5
## 64 BWin_v_BLose_L_NAcc_run1~1 -0.001 0.008 0.1
## 65 BWin_v_BLose_R_NAcc_run1~1 -0.003 0.003 0.9
## 66 AWin_v_Neut_L_NAcc_run2~1 -0.004 0.008 0.4
## 67 AWin_v_Neut_R_NAcc_run2~1 0.002 0.009 0.2
## 68 AWin_v_Neut_R_Insula_run2~1 0.006 0.006 0.7
## 69 BWin_v_Neut_L_NAcc_run2~1 -0.004 0.011 0.1
## 70 BWin_v_Neut_R_NAcc_run2~1 0.000 0.009 0.3
## 71 BWin_v_Neut_R_Insula_run2~1 -0.001 0.001 1.0
## 72 BWin_v_BLose_L_NAcc_run2~1 -0.007 0.007 0.4
## 73 BWin_v_BLose_R_NAcc_run2~1 -0.006 0.006 0.5
## 74 ALose_v_Neut_L_Insula_run1~1 -0.004 0.006 0.7
## 75 BLose_v_Neut_L_Insula_run1~1 0.004 0.001 1.0
## 76 BLose_v_Neut_R_Insula_run1~1 0.015 0.006 0.5
## 77 BLose_v_BWin_L_Insula_run1~1 0.008 0.010 0.0
## 78 BLose_v_BWin_R_Insula_run1~1 0.003 0.010 0.2
## 79 ALose_v_Neut_L_Insula_run2~1 -0.001 0.001 1.0
## 80 ALose_v_Neut_R_Insula_run2~1 0.001 0.008 0.4
## 81 BLose_v_Neut_L_Insula_run2~1 0.011 0.009 0.2
## 82 BLose_v_Neut_R_Insula_run2~1 0.005 0.007 0.4
## 83 BLose_v_BWin_L_Insula_run2~1 0.005 0.007 0.3
## 84 BLose_v_BWin_R_Insula_run2~1 -0.004 0.012 0.1
## 85 Approach~1 0.000 0.000 1.0
## 86 Avoid~1 0.000 0.000 1.0
## 87 rmsea 0.078 0.006 0.1
## 88 cfi 0.578 0.042 0.4
## 89 tli 0.541 0.046 0.4
## 90 gfi 0.857 0.015 0.2
## 91 srmr 0.063 0.004 0.4
## MAD MAD_p lin_slo lin_slo_p
## 1 0.014 0.0 0.015 0.0
## 2 0.007 0.7 -0.007 0.4
## 3 0.006 0.5 0.000 0.8
## 4 0.008 0.1 0.004 0.6
## 5 0.011 0.0 0.010 0.0
## 6 0.008 0.4 0.001 0.8
## 7 0.009 0.1 0.008 0.4
## 8 0.009 0.2 0.003 0.6
## 9 0.010 0.3 0.010 0.2
## 10 0.012 0.0 0.004 0.2
## 11 0.004 0.8 -0.002 1.0
## 12 0.005 0.5 0.002 0.6
## 13 0.006 0.4 0.004 0.4
## 14 0.011 0.1 0.002 1.0
## 15 0.005 0.7 0.001 0.4
## 16 0.013 0.2 0.012 0.2
## 17 0.009 0.5 -0.008 0.8
## 18 0.008 0.5 0.004 0.4
## 19 0.010 0.3 -0.003 0.4
## 20 0.009 0.2 -0.002 0.4
## 21 0.006 0.5 0.004 0.6
## 22 0.007 0.5 -0.005 0.4
## 23 0.006 0.4 -0.006 0.4
## 24 0.008 0.3 0.003 0.4
## 25 0.004 1.0 -0.002 0.6
## 26 0.004 0.9 -0.005 0.4
## 27 0.006 0.9 0.004 0.6
## 28 0.000 1.0 0.000 0.2
## 29 0.003 0.2 0.003 0.2
## 30 0.001 0.9 0.001 0.6
## 31 0.002 0.1 -0.001 0.2
## 32 0.001 0.8 0.000 0.4
## 33 0.002 0.4 0.002 0.2
## 34 0.002 0.1 0.000 0.6
## 35 0.001 0.7 0.001 0.6
## 36 0.001 0.7 -0.001 0.8
## 37 0.001 0.9 0.000 0.4
## 38 0.002 0.0 0.001 0.2
## 39 0.001 0.4 0.001 0.4
## 40 0.002 0.5 0.000 0.8
## 41 0.002 0.3 0.001 0.4
## 42 0.001 0.9 0.000 0.4
## 43 0.001 0.5 0.001 0.2
## 44 0.002 0.1 0.000 0.6
## 45 0.001 0.5 0.000 1.0
## 46 0.002 0.2 0.002 0.0
## 47 0.001 0.9 0.000 0.6
## 48 0.001 0.9 0.000 0.6
## 49 0.001 0.6 0.000 0.4
## 50 0.001 0.4 -0.001 0.2
## 51 0.002 0.4 -0.001 0.0
## 52 0.000 0.9 0.000 1.0
## 53 0.002 0.0 -0.002 0.4
## 54 0.000 1.0 0.000 1.0
## 55 0.000 1.0 0.000 1.0
## 56 0.000 1.0 0.000 1.0
## 57 0.029 0.4 -0.029 0.2
## 58 0.007 0.2 -0.002 0.4
## 59 0.005 0.7 0.004 0.8
## 60 0.002 0.8 0.001 0.6
## 61 0.011 0.0 0.009 0.0
## 62 0.005 0.7 -0.002 0.2
## 63 0.004 0.6 0.003 0.8
## 64 0.007 0.1 -0.007 0.0
## 65 0.002 0.9 -0.001 0.6
## 66 0.007 0.3 -0.001 0.8
## 67 0.007 0.3 0.007 0.2
## 68 0.005 0.7 0.001 0.6
## 69 0.010 0.2 0.007 0.4
## 70 0.007 0.4 0.005 0.2
## 71 0.001 1.0 0.001 0.4
## 72 0.006 0.3 -0.003 0.4
## 73 0.005 0.6 -0.004 0.2
## 74 0.005 0.8 -0.003 0.4
## 75 0.001 1.0 0.000 0.4
## 76 0.005 0.5 0.000 0.6
## 77 0.009 0.1 0.000 0.4
## 78 0.009 0.1 -0.005 0.2
## 79 0.001 1.0 -0.001 1.0
## 80 0.007 0.4 0.006 0.2
## 81 0.007 0.4 0.008 0.0
## 82 0.007 0.3 0.002 0.6
## 83 0.007 0.2 -0.001 1.0
## 84 0.011 0.1 -0.011 0.0
## 85 0.000 1.0 0.000 1.0
## 86 0.000 1.0 0.000 1.0
## 87 0.004 0.2 -0.004 0.2
## 88 0.040 0.2 0.039 0.4
## 89 0.044 0.2 0.042 0.4
## 90 0.011 0.2 0.011 0.0
## 91 0.003 0.1 -0.003 0.4
##
## Pointwise Test Statistics
##
## par parindex moderator
## 1 Approach=~AWin_v_Neut_L_NAcc_run1 1 1
## 2 Approach=~AWin_v_Neut_L_NAcc_run1 1 2
## 3 Approach=~AWin_v_Neut_L_NAcc_run1 1 3
## 4 Approach=~AWin_v_Neut_L_NAcc_run1 1 4
## 5 Approach=~AWin_v_Neut_L_NAcc_run1 1 5
## 6 Approach=~AWin_v_Neut_R_NAcc_run1 2 1
## 7 Approach=~AWin_v_Neut_R_NAcc_run1 2 2
## 8 Approach=~AWin_v_Neut_R_NAcc_run1 2 3
## 9 Approach=~AWin_v_Neut_R_NAcc_run1 2 4
## 10 Approach=~AWin_v_Neut_R_NAcc_run1 2 5
## 11 Approach=~AWin_v_Neut_R_Insula_run1 3 1
## 12 Approach=~AWin_v_Neut_R_Insula_run1 3 2
## 13 Approach=~AWin_v_Neut_R_Insula_run1 3 3
## 14 Approach=~AWin_v_Neut_R_Insula_run1 3 4
## 15 Approach=~AWin_v_Neut_R_Insula_run1 3 5
## 16 Approach=~BWin_v_Neut_L_NAcc_run1 4 1
## 17 Approach=~BWin_v_Neut_L_NAcc_run1 4 2
## 18 Approach=~BWin_v_Neut_L_NAcc_run1 4 3
## 19 Approach=~BWin_v_Neut_L_NAcc_run1 4 4
## 20 Approach=~BWin_v_Neut_L_NAcc_run1 4 5
## 21 Approach=~BWin_v_Neut_R_NAcc_run1 5 1
## 22 Approach=~BWin_v_Neut_R_NAcc_run1 5 2
## 23 Approach=~BWin_v_Neut_R_NAcc_run1 5 3
## 24 Approach=~BWin_v_Neut_R_NAcc_run1 5 4
## 25 Approach=~BWin_v_Neut_R_NAcc_run1 5 5
## 26 Approach=~BWin_v_Neut_R_Insula_run1 6 1
## 27 Approach=~BWin_v_Neut_R_Insula_run1 6 2
## 28 Approach=~BWin_v_Neut_R_Insula_run1 6 3
## 29 Approach=~BWin_v_Neut_R_Insula_run1 6 4
## 30 Approach=~BWin_v_Neut_R_Insula_run1 6 5
## 31 Approach=~BWin_v_BLose_L_NAcc_run1 7 1
## 32 Approach=~BWin_v_BLose_L_NAcc_run1 7 2
## 33 Approach=~BWin_v_BLose_L_NAcc_run1 7 3
## 34 Approach=~BWin_v_BLose_L_NAcc_run1 7 4
## 35 Approach=~BWin_v_BLose_L_NAcc_run1 7 5
## 36 Approach=~BWin_v_BLose_R_NAcc_run1 8 1
## 37 Approach=~BWin_v_BLose_R_NAcc_run1 8 2
## 38 Approach=~BWin_v_BLose_R_NAcc_run1 8 3
## 39 Approach=~BWin_v_BLose_R_NAcc_run1 8 4
## 40 Approach=~BWin_v_BLose_R_NAcc_run1 8 5
## 41 Approach=~AWin_v_Neut_L_NAcc_run2 9 1
## 42 Approach=~AWin_v_Neut_L_NAcc_run2 9 2
## 43 Approach=~AWin_v_Neut_L_NAcc_run2 9 3
## 44 Approach=~AWin_v_Neut_L_NAcc_run2 9 4
## 45 Approach=~AWin_v_Neut_L_NAcc_run2 9 5
## 46 Approach=~AWin_v_Neut_R_NAcc_run2 10 1
## 47 Approach=~AWin_v_Neut_R_NAcc_run2 10 2
## 48 Approach=~AWin_v_Neut_R_NAcc_run2 10 3
## 49 Approach=~AWin_v_Neut_R_NAcc_run2 10 4
## 50 Approach=~AWin_v_Neut_R_NAcc_run2 10 5
## 51 Approach=~AWin_v_Neut_R_Insula_run2 11 1
## 52 Approach=~AWin_v_Neut_R_Insula_run2 11 2
## 53 Approach=~AWin_v_Neut_R_Insula_run2 11 3
## 54 Approach=~AWin_v_Neut_R_Insula_run2 11 4
## 55 Approach=~AWin_v_Neut_R_Insula_run2 11 5
## 56 Approach=~BWin_v_Neut_L_NAcc_run2 12 1
## 57 Approach=~BWin_v_Neut_L_NAcc_run2 12 2
## 58 Approach=~BWin_v_Neut_L_NAcc_run2 12 3
## 59 Approach=~BWin_v_Neut_L_NAcc_run2 12 4
## 60 Approach=~BWin_v_Neut_L_NAcc_run2 12 5
## 61 Approach=~BWin_v_Neut_R_NAcc_run2 13 1
## 62 Approach=~BWin_v_Neut_R_NAcc_run2 13 2
## 63 Approach=~BWin_v_Neut_R_NAcc_run2 13 3
## 64 Approach=~BWin_v_Neut_R_NAcc_run2 13 4
## 65 Approach=~BWin_v_Neut_R_NAcc_run2 13 5
## 66 Approach=~BWin_v_Neut_R_Insula_run2 14 1
## 67 Approach=~BWin_v_Neut_R_Insula_run2 14 2
## 68 Approach=~BWin_v_Neut_R_Insula_run2 14 3
## 69 Approach=~BWin_v_Neut_R_Insula_run2 14 4
## 70 Approach=~BWin_v_Neut_R_Insula_run2 14 5
## 71 Approach=~BWin_v_BLose_L_NAcc_run2 15 1
## 72 Approach=~BWin_v_BLose_L_NAcc_run2 15 2
## 73 Approach=~BWin_v_BLose_L_NAcc_run2 15 3
## 74 Approach=~BWin_v_BLose_L_NAcc_run2 15 4
## 75 Approach=~BWin_v_BLose_L_NAcc_run2 15 5
## 76 Approach=~BWin_v_BLose_R_NAcc_run2 16 1
## 77 Approach=~BWin_v_BLose_R_NAcc_run2 16 2
## 78 Approach=~BWin_v_BLose_R_NAcc_run2 16 3
## 79 Approach=~BWin_v_BLose_R_NAcc_run2 16 4
## 80 Approach=~BWin_v_BLose_R_NAcc_run2 16 5
## 81 Avoid=~ALose_v_Neut_L_Insula_run1 17 1
## 82 Avoid=~ALose_v_Neut_L_Insula_run1 17 2
## 83 Avoid=~ALose_v_Neut_L_Insula_run1 17 3
## 84 Avoid=~ALose_v_Neut_L_Insula_run1 17 4
## 85 Avoid=~ALose_v_Neut_L_Insula_run1 17 5
## 86 Avoid=~BLose_v_Neut_L_Insula_run1 18 1
## 87 Avoid=~BLose_v_Neut_L_Insula_run1 18 2
## 88 Avoid=~BLose_v_Neut_L_Insula_run1 18 3
## 89 Avoid=~BLose_v_Neut_L_Insula_run1 18 4
## 90 Avoid=~BLose_v_Neut_L_Insula_run1 18 5
## 91 Avoid=~BLose_v_Neut_R_Insula_run1 19 1
## 92 Avoid=~BLose_v_Neut_R_Insula_run1 19 2
## 93 Avoid=~BLose_v_Neut_R_Insula_run1 19 3
## 94 Avoid=~BLose_v_Neut_R_Insula_run1 19 4
## 95 Avoid=~BLose_v_Neut_R_Insula_run1 19 5
## 96 Avoid=~BLose_v_BWin_L_Insula_run1 20 1
## 97 Avoid=~BLose_v_BWin_L_Insula_run1 20 2
## 98 Avoid=~BLose_v_BWin_L_Insula_run1 20 3
## 99 Avoid=~BLose_v_BWin_L_Insula_run1 20 4
## 100 Avoid=~BLose_v_BWin_L_Insula_run1 20 5
## 101 Avoid=~BLose_v_BWin_R_Insula_run1 21 1
## 102 Avoid=~BLose_v_BWin_R_Insula_run1 21 2
## 103 Avoid=~BLose_v_BWin_R_Insula_run1 21 3
## 104 Avoid=~BLose_v_BWin_R_Insula_run1 21 4
## 105 Avoid=~BLose_v_BWin_R_Insula_run1 21 5
## 106 Avoid=~ALose_v_Neut_L_Insula_run2 22 1
## 107 Avoid=~ALose_v_Neut_L_Insula_run2 22 2
## 108 Avoid=~ALose_v_Neut_L_Insula_run2 22 3
## 109 Avoid=~ALose_v_Neut_L_Insula_run2 22 4
## 110 Avoid=~ALose_v_Neut_L_Insula_run2 22 5
## 111 Avoid=~ALose_v_Neut_R_Insula_run2 23 1
## 112 Avoid=~ALose_v_Neut_R_Insula_run2 23 2
## 113 Avoid=~ALose_v_Neut_R_Insula_run2 23 3
## 114 Avoid=~ALose_v_Neut_R_Insula_run2 23 4
## 115 Avoid=~ALose_v_Neut_R_Insula_run2 23 5
## 116 Avoid=~BLose_v_Neut_L_Insula_run2 24 1
## 117 Avoid=~BLose_v_Neut_L_Insula_run2 24 2
## 118 Avoid=~BLose_v_Neut_L_Insula_run2 24 3
## 119 Avoid=~BLose_v_Neut_L_Insula_run2 24 4
## 120 Avoid=~BLose_v_Neut_L_Insula_run2 24 5
## 121 Avoid=~BLose_v_Neut_R_Insula_run2 25 1
## 122 Avoid=~BLose_v_Neut_R_Insula_run2 25 2
## 123 Avoid=~BLose_v_Neut_R_Insula_run2 25 3
## 124 Avoid=~BLose_v_Neut_R_Insula_run2 25 4
## 125 Avoid=~BLose_v_Neut_R_Insula_run2 25 5
## 126 Avoid=~BLose_v_BWin_L_Insula_run2 26 1
## 127 Avoid=~BLose_v_BWin_L_Insula_run2 26 2
## 128 Avoid=~BLose_v_BWin_L_Insula_run2 26 3
## 129 Avoid=~BLose_v_BWin_L_Insula_run2 26 4
## 130 Avoid=~BLose_v_BWin_L_Insula_run2 26 5
## 131 Avoid=~BLose_v_BWin_R_Insula_run2 27 1
## 132 Avoid=~BLose_v_BWin_R_Insula_run2 27 2
## 133 Avoid=~BLose_v_BWin_R_Insula_run2 27 3
## 134 Avoid=~BLose_v_BWin_R_Insula_run2 27 4
## 135 Avoid=~BLose_v_BWin_R_Insula_run2 27 5
## 136 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 1
## 137 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 2
## 138 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 3
## 139 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 4
## 140 AWin_v_Neut_L_NAcc_run1~~AWin_v_Neut_L_NAcc_run1 28 5
## 141 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 1
## 142 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 2
## 143 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 3
## 144 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 4
## 145 AWin_v_Neut_R_NAcc_run1~~AWin_v_Neut_R_NAcc_run1 29 5
## 146 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 1
## 147 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 2
## 148 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 3
## 149 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 4
## 150 AWin_v_Neut_R_Insula_run1~~AWin_v_Neut_R_Insula_run1 30 5
## 151 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 1
## 152 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 2
## 153 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 3
## 154 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 4
## 155 BWin_v_Neut_L_NAcc_run1~~BWin_v_Neut_L_NAcc_run1 31 5
## 156 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 1
## 157 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 2
## 158 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 3
## 159 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 4
## 160 BWin_v_Neut_R_NAcc_run1~~BWin_v_Neut_R_NAcc_run1 32 5
## 161 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 1
## 162 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 2
## 163 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 3
## 164 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 4
## 165 BWin_v_Neut_R_Insula_run1~~BWin_v_Neut_R_Insula_run1 33 5
## 166 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 1
## 167 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 2
## 168 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 3
## 169 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 4
## 170 BWin_v_BLose_L_NAcc_run1~~BWin_v_BLose_L_NAcc_run1 34 5
## 171 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 1
## 172 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 2
## 173 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 3
## 174 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 4
## 175 BWin_v_BLose_R_NAcc_run1~~BWin_v_BLose_R_NAcc_run1 35 5
## 176 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 1
## 177 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 2
## 178 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 3
## 179 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 4
## 180 AWin_v_Neut_L_NAcc_run2~~AWin_v_Neut_L_NAcc_run2 36 5
## 181 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 1
## 182 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 2
## 183 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 3
## 184 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 4
## 185 AWin_v_Neut_R_NAcc_run2~~AWin_v_Neut_R_NAcc_run2 37 5
## 186 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 1
## 187 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 2
## 188 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 3
## 189 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 4
## 190 AWin_v_Neut_R_Insula_run2~~AWin_v_Neut_R_Insula_run2 38 5
## 191 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 1
## 192 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 2
## 193 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 3
## 194 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 4
## 195 BWin_v_Neut_L_NAcc_run2~~BWin_v_Neut_L_NAcc_run2 39 5
## 196 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 1
## 197 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 2
## 198 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 3
## 199 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 4
## 200 BWin_v_Neut_R_NAcc_run2~~BWin_v_Neut_R_NAcc_run2 40 5
## 201 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 1
## 202 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 2
## 203 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 3
## 204 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 4
## 205 BWin_v_Neut_R_Insula_run2~~BWin_v_Neut_R_Insula_run2 41 5
## 206 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 1
## 207 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 2
## 208 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 3
## 209 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 4
## 210 BWin_v_BLose_L_NAcc_run2~~BWin_v_BLose_L_NAcc_run2 42 5
## 211 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 1
## 212 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 2
## 213 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 3
## 214 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 4
## 215 BWin_v_BLose_R_NAcc_run2~~BWin_v_BLose_R_NAcc_run2 43 5
## 216 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 1
## 217 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 2
## 218 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 3
## 219 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 4
## 220 ALose_v_Neut_L_Insula_run1~~ALose_v_Neut_L_Insula_run1 44 5
## 221 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 1
## 222 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 2
## 223 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 3
## 224 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 4
## 225 BLose_v_Neut_L_Insula_run1~~BLose_v_Neut_L_Insula_run1 45 5
## 226 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 1
## 227 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 2
## 228 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 3
## 229 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 4
## 230 BLose_v_Neut_R_Insula_run1~~BLose_v_Neut_R_Insula_run1 46 5
## 231 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 1
## 232 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 2
## 233 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 3
## 234 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 4
## 235 BLose_v_BWin_L_Insula_run1~~BLose_v_BWin_L_Insula_run1 47 5
## 236 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 1
## 237 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 2
## 238 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 3
## 239 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 4
## 240 BLose_v_BWin_R_Insula_run1~~BLose_v_BWin_R_Insula_run1 48 5
## 241 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 1
## 242 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 2
## 243 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 3
## 244 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 4
## 245 ALose_v_Neut_L_Insula_run2~~ALose_v_Neut_L_Insula_run2 49 5
## 246 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 1
## 247 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 2
## 248 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 3
## 249 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 4
## 250 ALose_v_Neut_R_Insula_run2~~ALose_v_Neut_R_Insula_run2 50 5
## 251 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 1
## 252 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 2
## 253 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 3
## 254 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 4
## 255 BLose_v_Neut_L_Insula_run2~~BLose_v_Neut_L_Insula_run2 51 5
## 256 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 1
## 257 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 2
## 258 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 3
## 259 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 4
## 260 BLose_v_Neut_R_Insula_run2~~BLose_v_Neut_R_Insula_run2 52 5
## 261 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 1
## 262 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 2
## 263 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 3
## 264 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 4
## 265 BLose_v_BWin_L_Insula_run2~~BLose_v_BWin_L_Insula_run2 53 5
## 266 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 1
## 267 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 2
## 268 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 3
## 269 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 4
## 270 BLose_v_BWin_R_Insula_run2~~BLose_v_BWin_R_Insula_run2 54 5
## 271 Approach~~Approach 55 1
## 272 Approach~~Approach 55 2
## 273 Approach~~Approach 55 3
## 274 Approach~~Approach 55 4
## 275 Approach~~Approach 55 5
## 276 Avoid~~Avoid 56 1
## 277 Avoid~~Avoid 56 2
## 278 Avoid~~Avoid 56 3
## 279 Avoid~~Avoid 56 4
## 280 Avoid~~Avoid 56 5
## 281 Approach~~Avoid 57 1
## 282 Approach~~Avoid 57 2
## 283 Approach~~Avoid 57 3
## 284 Approach~~Avoid 57 4
## 285 Approach~~Avoid 57 5
## 286 AWin_v_Neut_L_NAcc_run1~1 58 1
## 287 AWin_v_Neut_L_NAcc_run1~1 58 2
## 288 AWin_v_Neut_L_NAcc_run1~1 58 3
## 289 AWin_v_Neut_L_NAcc_run1~1 58 4
## 290 AWin_v_Neut_L_NAcc_run1~1 58 5
## 291 AWin_v_Neut_R_NAcc_run1~1 59 1
## 292 AWin_v_Neut_R_NAcc_run1~1 59 2
## 293 AWin_v_Neut_R_NAcc_run1~1 59 3
## 294 AWin_v_Neut_R_NAcc_run1~1 59 4
## 295 AWin_v_Neut_R_NAcc_run1~1 59 5
## 296 AWin_v_Neut_R_Insula_run1~1 60 1
## 297 AWin_v_Neut_R_Insula_run1~1 60 2
## 298 AWin_v_Neut_R_Insula_run1~1 60 3
## 299 AWin_v_Neut_R_Insula_run1~1 60 4
## 300 AWin_v_Neut_R_Insula_run1~1 60 5
## 301 BWin_v_Neut_L_NAcc_run1~1 61 1
## 302 BWin_v_Neut_L_NAcc_run1~1 61 2
## 303 BWin_v_Neut_L_NAcc_run1~1 61 3
## 304 BWin_v_Neut_L_NAcc_run1~1 61 4
## 305 BWin_v_Neut_L_NAcc_run1~1 61 5
## 306 BWin_v_Neut_R_NAcc_run1~1 62 1
## 307 BWin_v_Neut_R_NAcc_run1~1 62 2
## 308 BWin_v_Neut_R_NAcc_run1~1 62 3
## 309 BWin_v_Neut_R_NAcc_run1~1 62 4
## 310 BWin_v_Neut_R_NAcc_run1~1 62 5
## 311 BWin_v_Neut_R_Insula_run1~1 63 1
## 312 BWin_v_Neut_R_Insula_run1~1 63 2
## 313 BWin_v_Neut_R_Insula_run1~1 63 3
## 314 BWin_v_Neut_R_Insula_run1~1 63 4
## 315 BWin_v_Neut_R_Insula_run1~1 63 5
## 316 BWin_v_BLose_L_NAcc_run1~1 64 1
## 317 BWin_v_BLose_L_NAcc_run1~1 64 2
## 318 BWin_v_BLose_L_NAcc_run1~1 64 3
## 319 BWin_v_BLose_L_NAcc_run1~1 64 4
## 320 BWin_v_BLose_L_NAcc_run1~1 64 5
## 321 BWin_v_BLose_R_NAcc_run1~1 65 1
## 322 BWin_v_BLose_R_NAcc_run1~1 65 2
## 323 BWin_v_BLose_R_NAcc_run1~1 65 3
## 324 BWin_v_BLose_R_NAcc_run1~1 65 4
## 325 BWin_v_BLose_R_NAcc_run1~1 65 5
## 326 AWin_v_Neut_L_NAcc_run2~1 66 1
## 327 AWin_v_Neut_L_NAcc_run2~1 66 2
## 328 AWin_v_Neut_L_NAcc_run2~1 66 3
## 329 AWin_v_Neut_L_NAcc_run2~1 66 4
## 330 AWin_v_Neut_L_NAcc_run2~1 66 5
## 331 AWin_v_Neut_R_NAcc_run2~1 67 1
## 332 AWin_v_Neut_R_NAcc_run2~1 67 2
## 333 AWin_v_Neut_R_NAcc_run2~1 67 3
## 334 AWin_v_Neut_R_NAcc_run2~1 67 4
## 335 AWin_v_Neut_R_NAcc_run2~1 67 5
## 336 AWin_v_Neut_R_Insula_run2~1 68 1
## 337 AWin_v_Neut_R_Insula_run2~1 68 2
## 338 AWin_v_Neut_R_Insula_run2~1 68 3
## 339 AWin_v_Neut_R_Insula_run2~1 68 4
## 340 AWin_v_Neut_R_Insula_run2~1 68 5
## 341 BWin_v_Neut_L_NAcc_run2~1 69 1
## 342 BWin_v_Neut_L_NAcc_run2~1 69 2
## 343 BWin_v_Neut_L_NAcc_run2~1 69 3
## 344 BWin_v_Neut_L_NAcc_run2~1 69 4
## 345 BWin_v_Neut_L_NAcc_run2~1 69 5
## 346 BWin_v_Neut_R_NAcc_run2~1 70 1
## 347 BWin_v_Neut_R_NAcc_run2~1 70 2
## 348 BWin_v_Neut_R_NAcc_run2~1 70 3
## 349 BWin_v_Neut_R_NAcc_run2~1 70 4
## 350 BWin_v_Neut_R_NAcc_run2~1 70 5
## 351 BWin_v_Neut_R_Insula_run2~1 71 1
## 352 BWin_v_Neut_R_Insula_run2~1 71 2
## 353 BWin_v_Neut_R_Insula_run2~1 71 3
## 354 BWin_v_Neut_R_Insula_run2~1 71 4
## 355 BWin_v_Neut_R_Insula_run2~1 71 5
## 356 BWin_v_BLose_L_NAcc_run2~1 72 1
## 357 BWin_v_BLose_L_NAcc_run2~1 72 2
## 358 BWin_v_BLose_L_NAcc_run2~1 72 3
## 359 BWin_v_BLose_L_NAcc_run2~1 72 4
## 360 BWin_v_BLose_L_NAcc_run2~1 72 5
## 361 BWin_v_BLose_R_NAcc_run2~1 73 1
## 362 BWin_v_BLose_R_NAcc_run2~1 73 2
## 363 BWin_v_BLose_R_NAcc_run2~1 73 3
## 364 BWin_v_BLose_R_NAcc_run2~1 73 4
## 365 BWin_v_BLose_R_NAcc_run2~1 73 5
## 366 ALose_v_Neut_L_Insula_run1~1 74 1
## 367 ALose_v_Neut_L_Insula_run1~1 74 2
## 368 ALose_v_Neut_L_Insula_run1~1 74 3
## 369 ALose_v_Neut_L_Insula_run1~1 74 4
## 370 ALose_v_Neut_L_Insula_run1~1 74 5
## 371 BLose_v_Neut_L_Insula_run1~1 75 1
## 372 BLose_v_Neut_L_Insula_run1~1 75 2
## 373 BLose_v_Neut_L_Insula_run1~1 75 3
## 374 BLose_v_Neut_L_Insula_run1~1 75 4
## 375 BLose_v_Neut_L_Insula_run1~1 75 5
## 376 BLose_v_Neut_R_Insula_run1~1 76 1
## 377 BLose_v_Neut_R_Insula_run1~1 76 2
## 378 BLose_v_Neut_R_Insula_run1~1 76 3
## 379 BLose_v_Neut_R_Insula_run1~1 76 4
## 380 BLose_v_Neut_R_Insula_run1~1 76 5
## 381 BLose_v_BWin_L_Insula_run1~1 77 1
## 382 BLose_v_BWin_L_Insula_run1~1 77 2
## 383 BLose_v_BWin_L_Insula_run1~1 77 3
## 384 BLose_v_BWin_L_Insula_run1~1 77 4
## 385 BLose_v_BWin_L_Insula_run1~1 77 5
## 386 BLose_v_BWin_R_Insula_run1~1 78 1
## 387 BLose_v_BWin_R_Insula_run1~1 78 2
## 388 BLose_v_BWin_R_Insula_run1~1 78 3
## 389 BLose_v_BWin_R_Insula_run1~1 78 4
## 390 BLose_v_BWin_R_Insula_run1~1 78 5
## 391 ALose_v_Neut_L_Insula_run2~1 79 1
## 392 ALose_v_Neut_L_Insula_run2~1 79 2
## 393 ALose_v_Neut_L_Insula_run2~1 79 3
## 394 ALose_v_Neut_L_Insula_run2~1 79 4
## 395 ALose_v_Neut_L_Insula_run2~1 79 5
## 396 ALose_v_Neut_R_Insula_run2~1 80 1
## 397 ALose_v_Neut_R_Insula_run2~1 80 2
## 398 ALose_v_Neut_R_Insula_run2~1 80 3
## 399 ALose_v_Neut_R_Insula_run2~1 80 4
## 400 ALose_v_Neut_R_Insula_run2~1 80 5
## 401 BLose_v_Neut_L_Insula_run2~1 81 1
## 402 BLose_v_Neut_L_Insula_run2~1 81 2
## 403 BLose_v_Neut_L_Insula_run2~1 81 3
## 404 BLose_v_Neut_L_Insula_run2~1 81 4
## 405 BLose_v_Neut_L_Insula_run2~1 81 5
## 406 BLose_v_Neut_R_Insula_run2~1 82 1
## 407 BLose_v_Neut_R_Insula_run2~1 82 2
## 408 BLose_v_Neut_R_Insula_run2~1 82 3
## 409 BLose_v_Neut_R_Insula_run2~1 82 4
## 410 BLose_v_Neut_R_Insula_run2~1 82 5
## 411 BLose_v_BWin_L_Insula_run2~1 83 1
## 412 BLose_v_BWin_L_Insula_run2~1 83 2
## 413 BLose_v_BWin_L_Insula_run2~1 83 3
## 414 BLose_v_BWin_L_Insula_run2~1 83 4
## 415 BLose_v_BWin_L_Insula_run2~1 83 5
## 416 BLose_v_BWin_R_Insula_run2~1 84 1
## 417 BLose_v_BWin_R_Insula_run2~1 84 2
## 418 BLose_v_BWin_R_Insula_run2~1 84 3
## 419 BLose_v_BWin_R_Insula_run2~1 84 4
## 420 BLose_v_BWin_R_Insula_run2~1 84 5
## 421 Approach~1 85 1
## 422 Approach~1 85 2
## 423 Approach~1 85 3
## 424 Approach~1 85 4
## 425 Approach~1 85 5
## 426 Avoid~1 86 1
## 427 Avoid~1 86 2
## 428 Avoid~1 86 3
## 429 Avoid~1 86 4
## 430 Avoid~1 86 5
## 431 rmsea 87 1
## 432 rmsea 87 2
## 433 rmsea 87 3
## 434 rmsea 87 4
## 435 rmsea 87 5
## 436 cfi 88 1
## 437 cfi 88 2
## 438 cfi 88 3
## 439 cfi 88 4
## 440 cfi 88 5
## 441 tli 89 1
## 442 tli 89 2
## 443 tli 89 3
## 444 tli 89 4
## 445 tli 89 5
## 446 gfi 90 1
## 447 gfi 90 2
## 448 gfi 90 3
## 449 gfi 90 4
## 450 gfi 90 5
## 451 srmr 91 1
## 452 srmr 91 2
## 453 srmr 91 3
## 454 srmr 91 4
## 455 srmr 91 5
## est p
## 1 -0.017 0.4
## 2 -0.017 0.0
## 3 0.005 0.8
## 4 0.021 0.0
## 5 0.023 0.0
## 6 0.024 0.2
## 7 -0.002 0.8
## 8 -0.006 0.8
## 9 -0.003 0.4
## 10 -0.003 0.4
## 11 -0.007 0.2
## 12 0.009 0.2
## 13 -0.006 0.4
## 14 0.002 0.8
## 15 0.003 0.4
## 16 0.007 0.2
## 17 -0.009 0.0
## 18 -0.006 0.2
## 19 0.012 0.0
## 20 0.015 0.0
## 21 -0.023 0.2
## 22 0.003 0.4
## 23 -0.007 0.2
## 24 0.018 0.0
## 25 0.022 0.0
## 26 0.006 0.8
## 27 0.001 0.8
## 28 -0.012 0.2
## 29 0.010 0.0
## 30 0.014 0.0
## 31 -0.004 0.4
## 32 -0.014 0.0
## 33 0.006 0.4
## 34 0.010 0.6
## 35 0.011 0.8
## 36 0.008 0.8
## 37 -0.008 0.2
## 38 -0.007 0.4
## 39 0.012 0.0
## 40 0.016 0.2
## 41 -0.008 1.0
## 42 -0.014 0.2
## 43 0.004 0.8
## 44 0.015 0.2
## 45 0.016 0.2
## 46 0.015 0.0
## 47 -0.017 0.0
## 48 -0.004 0.6
## 49 0.015 0.0
## 50 0.018 0.2
## 51 -0.005 0.4
## 52 0.007 0.2
## 53 0.000 1.0
## 54 -0.005 0.6
## 55 -0.005 0.6
## 56 0.001 0.4
## 57 -0.008 0.0
## 58 0.007 0.8
## 59 -0.001 0.6
## 60 -0.003 0.6
## 61 -0.010 0.6
## 62 0.002 0.4
## 63 -0.005 0.0
## 64 0.009 0.2
## 65 0.011 0.2
## 66 0.013 0.8
## 67 -0.007 0.4
## 68 -0.010 0.2
## 69 0.014 0.0
## 70 0.018 0.0
## 71 0.009 0.2
## 72 -0.006 0.8
## 73 -0.002 0.6
## 74 0.005 1.0
## 75 0.006 0.8
## 76 -0.027 0.0
## 77 -0.009 0.6
## 78 0.014 0.0
## 79 0.007 0.8
## 80 0.006 0.6
## 81 0.010 0.8
## 82 0.012 0.4
## 83 -0.009 0.4
## 84 -0.008 0.8
## 85 -0.008 0.8
## 86 0.009 0.6
## 87 -0.014 0.0
## 88 0.001 1.0
## 89 0.009 0.2
## 90 0.011 0.2
## 91 -0.009 0.4
## 92 0.009 0.0
## 93 0.008 0.6
## 94 -0.013 0.4
## 95 -0.017 0.4
## 96 0.018 0.2
## 97 -0.004 0.8
## 98 -0.011 0.0
## 99 0.007 0.6
## 100 0.012 0.2
## 101 -0.002 0.8
## 102 -0.009 0.2
## 103 0.005 0.0
## 104 0.004 0.6
## 105 0.003 0.8
## 106 0.012 0.4
## 107 0.005 0.6
## 108 -0.010 0.2
## 109 0.001 0.8
## 110 0.003 0.8
## 111 0.020 0.4
## 112 -0.003 0.4
## 113 -0.004 0.8
## 114 -0.002 0.8
## 115 -0.002 0.8
## 116 0.009 0.8
## 117 -0.015 0.0
## 118 0.004 0.4
## 119 0.006 0.4
## 120 0.006 0.4
## 121 0.001 0.8
## 122 0.006 0.6
## 123 -0.006 0.2
## 124 0.001 0.6
## 125 0.002 0.6
## 126 0.014 0.4
## 127 0.000 0.6
## 128 -0.002 0.6
## 129 -0.006 0.6
## 130 -0.006 0.6
## 131 -0.006 0.8
## 132 -0.007 0.4
## 133 0.007 0.0
## 134 0.002 0.8
## 135 0.000 0.8
## 136 -0.001 0.6
## 137 0.000 0.6
## 138 0.001 1.0
## 139 0.000 0.4
## 140 0.000 0.2
## 141 -0.006 0.0
## 142 -0.001 0.6
## 143 0.000 0.6
## 144 0.005 0.2
## 145 0.006 0.2
## 146 -0.002 0.2
## 147 0.001 1.0
## 148 0.000 1.0
## 149 0.000 0.8
## 150 0.000 0.6
## 151 -0.001 0.6
## 152 0.003 0.0
## 153 0.000 1.0
## 154 -0.002 0.4
## 155 -0.002 0.4
## 156 -0.001 0.8
## 157 0.000 0.2
## 158 0.001 0.6
## 159 -0.001 0.6
## 160 -0.002 0.6
## 161 -0.006 0.0
## 162 0.000 1.0
## 163 0.002 0.0
## 164 0.001 0.8
## 165 0.000 0.6
## 166 -0.004 0.0
## 167 0.003 0.0
## 168 -0.001 0.0
## 169 0.000 0.6
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